Real-Time Well Construction Process Inference Through Probabilistic Data Fusion

ABSTRACT

A method includes acquiring data during rig operations where the rig operations include operations that utilize a bit to drill rock and where the data include different types of data; analyzing the data utilizing a probabilistic mixture model for modes, a detection engine for trends and a network model for an inference based at least in part on at least one of a mode and a trend; and outputting information as to the inference where the inference characterizes a relationship between the bit and the rock

RELATED APPLICATIONS

This application claims priority to and the benefit of a US ProvisionalApplication having Ser. No. 62/525,481, filed 27 Jun. 2017, which isincorporated by reference herein. This application incorporates byreference herein a US Provisional Application having Ser. No.62/437,619, filed 21 Dec. 2016, and a U.S. Non-Provisional Applicationhaving Ser. No. 15/846,661, filed 19 Dec. 2017 (Attorney Docket No.IS16.1260).

BACKGROUND

A resource field can be an accumulation, pool or group of pools of oneor more resources (e.g., oil, gas, oil and gas) in a subsurfaceenvironment. A resource field can include at least one reservoir. Areservoir may be shaped in a manner that can trap hydrocarbons and maybe covered by an impermeable or sealing rock. A bore can be drilled intoan environment where the bore may be utilized to form a well that can beutilized in producing hydrocarbons from a reservoir.

A rig can be a system of components that can be operated to form a borein an environment, to transport equipment into and out of a bore in anenvironment, etc. As an example, a rig can include a system that can beused to drill a bore and to acquire information about an environment,about drilling, etc. A resource field may be an onshore field, anoffshore field or an on- and offshore field. A rig can includecomponents for performing operations onshore and/or offshore. A rig maybe, for example, vessel-based, offshore platform-based, onshore, etc.

Field planning can occur over one or more phases, which can include anexploration phase that aims to identify and assess an environment (e.g.,a prospect, a play, etc.), which may include drilling of one or morebores (e.g., one or more exploratory wells, etc.). Other phases caninclude appraisal, development and production phases.

SUMMARY

A method can include acquiring data during rig operations where the rigoperations include operations that utilize a bit to drill rock and wherethe data include different types of data; analyzing the data utilizing aprobabilistic mixture model for modes, a detection engine for trends anda network model for an inference based at least in part on at least oneof a mode and a trend; and outputting information as to the inferencewhere the inference characterizes a relationship between the bit and therock. A system can include a processor; memory accessible to theprocessor; processor-executable instructions stored in the memory andexecutable by the processor to instruct the system to: acquire dataduring rig operations where the rig operations include operations thatutilize a bit to drill rock and where the data include different typesof data; analyze the data utilizing a probabilistic mixture model formodes, a detection engine for trends and a network model for aninference based at least in part on at least one of a mode and a trend;and output information as to the inference where the inferencecharacterizes a relationship between the bit and the rock. One or morecomputer-readable storage media can include computer-executableinstructions, executable to instruct a computer to: acquire data duringrig operations where the rig operations include operations that utilizea bit to drill rock and where the data include different types of data;analyze the data utilizing a probabilistic mixture model for modes, adetection engine for trends and a network model for an inference basedat least in part on at least one of a mode and a trend; and outputinformation as to the inference where the inference characterizes arelationship between the bit and the rock. Various other apparatuses,systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates examples of equipment in a geologic environment;

FIG. 2 illustrates an example of a system and examples of types ofholes;

FIG. 3 illustrates an example of a system;

FIG. 4 illustrates an example of a system;

FIG. 5 illustrates an example of a system;

FIG. 6 illustrates an example of a system and an example of a scenario;

FIG. 7 illustrates an example of a wellsite system;

FIG. 8 illustrates an example of a system;

FIG. 9 illustrates an example of a system;

FIG. 10 illustrates an example of a method;

FIG. 11 illustrates an example of a system;

FIG. 12 illustrates an example of a graphical user interface;

FIG. 13 illustrates an example of a graphical user interface;

FIG. 14 illustrates an example of a graphical user interface;

FIG. 15 illustrates an example of a system;

FIG. 16 illustrates examples of graphical user interfaces;

FIG. 17 illustrates examples of graphical user interfaces;

FIG. 18 illustrates examples of graphical user interfaces;

FIG. 19 illustrates examples of graphical user interfaces;

FIG. 20 illustrates examples of graphical user interfaces;

FIG. 21 illustrates an example of a graphical user interface;

FIG. 22 illustrates examples of graphical user interfaces;

FIG. 23 illustrates an example of a method that includes a Bayesianbelief network;

FIG. 24 illustrates an example of a method that includes a Bayesianbelief network;

FIG. 25 illustrates an example of a method that includes a Bayesianbelief network;

FIG. 26 illustrates an example of a system;

FIG. 27 illustrates an example of a graphical user interface;

FIG. 28 illustrates an example of a graphical user interface;

FIG. 29 illustrates examples of computing and networking equipment; and

FIG. 30 illustrates example components of a system and a networkedsystem.

DETAILED DESCRIPTION

The following description includes embodiments of the best modepresently contemplated for practicing the described implementations.This description is not to be taken in a limiting sense, but rather ismade merely for the purpose of describing the general principles of theimplementations. The scope of the described implementations should beascertained with reference to the issued claims.

Well planning is a part of a well construction process by which a pathof a well can be mapped, so as to reach a reservoir, for example, toproduce fluids therefrom. As an example, constraints can be imposed on adesign of a well, for example, a well trajectory may be constrained viaone or more physical phenomena that may impact viability of a bore, easeof drilling, etc. Thus, for example, one or more constraints may beimposed based at least in part on known geology of a subterranean domainor, for example, presence of other wells in the area (e.g., collisionavoidance). As an example, one or more other constraints may be imposed,for example, consider one or more constraints germane to capabilities oftools being used and/or one or more constraints related to drilling timeand risk tolerance.

As an example, a well plan can be generated based at least in part onimposed constraints and known information. As an example, a well planmay be provided to a well owner, approved, and then implemented by adrilling service provider (e.g., a directional driller or “DD”).

As an example, a well design system can account for one or morecapabilities of a drilling system or drilling systems that may beutilized at a wellsite. As an example, a drilling engineer may be calledupon to take such capabilities into account, for example, as one or moreof various designs and specifications are created.

As an example, a well design system, which may be a well planningsystem, may take into account automation. For example, where a wellsiteincludes wellsite equipment that can be automated, for example, via alocal and/or a remote automation command, a well plan may be generatedin digital form that can be utilized in a well drilling system where atleast some amount of automation is possible and desired. For example, adigital well plan can be accessible by a well drilling system whereinformation in the digital well plan can be utilized via one or moreautomation mechanisms of the well drilling system to automate one ormore operations at a wellsite.

FIG. 1 shows an example of a geologic environment 120. In FIG. 1, thegeologic environment 120 may be a sedimentary basin that includes layers(e.g., stratification) that include a reservoir 121 and that may be, forexample, intersected by a fault 123 (e.g., or faults). As an example,the geologic environment 120 may be outfitted with any of a variety ofsensors, detectors, actuators, etc. For example, equipment 122 mayinclude communication circuitry to receive and/or to transmitinformation with respect to one or more networks 125. Such informationmay include information associated with downhole equipment 124, whichmay be equipment to acquire information, to assist with resourcerecovery, etc. Other equipment 126 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Suchequipment may include storage and communication circuitry to store andto communicate data, instructions, etc. As an example, one or morepieces of equipment may provide for measurement, collection,communication, storage, analysis, etc. of data (e.g., for one or moreproduced resources, etc.). As an example, one or more satellites may beprovided for purposes of communications, data acquisition, geolocation,etc. For example, FIG. 1 shows a satellite in communication with thenetwork 125 that may be configured for communications, noting that thesatellite may additionally or alternatively include circuitry forimagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 120 as optionally includingequipment 127 and 128 associated with a well that includes asubstantially horizontal portion that may intersect with one or morefractures 129. For example, consider a well in a shale formation thatmay include natural fractures, artificial fractures (e.g., hydraulicfractures) or a combination of natural and artificial fractures. As anexample, a well may be drilled for a reservoir that is laterallyextensive. In such an example, lateral variations in properties,stresses, etc. may exist where an assessment of such variations mayassist with planning, operations, etc. to develop the reservoir (e.g.,via fracturing, injecting, extracting, etc.). As an example, theequipment 127 and/or 128 may include components, a system, systems, etc.for fracturing, seismic sensing, analysis of seismic data, assessment ofone or more fractures, injection, production, etc. As an example, theequipment 127 and/or 128 may provide for measurement, collection,communication, storage, analysis, etc. of data such as, for example,production data (e.g., for one or more produced resources). As anexample, one or more satellites may be provided for purposes ofcommunications, data acquisition, etc.

FIG. 1 also shows an example of equipment 170 and an example ofequipment 180. Such equipment, which may be systems of components, maybe suitable for use in the geologic environment 120. While the equipment170 and 180 are illustrated as land-based, various components may besuitable for use in an offshore system. As shown in FIG. 1, theequipment 180 can be mobile as carried by a vehicle; noting that theequipment 170 can be assembled, disassembled, transported andre-assembled, etc.

The equipment 170 includes a platform 171, a derrick 172, a crown block173, a line 174, a traveling block assembly 175, drawworks 176 and alanding 177 (e.g., a monkeyboard). As an example, the line 174 may becontrolled at least in part via the drawworks 176 such that thetraveling block assembly 175 travels in a vertical direction withrespect to the platform 171. For example, by drawing the line 174 in,the drawworks 176 may cause the line 174 to run through the crown block173 and lift the traveling block assembly 175 skyward away from theplatform 171; whereas, by allowing the line 174 out, the drawworks 176may cause the line 174 to run through the crown block 173 and lower thetraveling block assembly 175 toward the platform 171. Where thetraveling block assembly 175 carries pipe (e.g., casing, etc.), trackingof movement of the traveling block 175 may provide an indication as tohow much pipe has been deployed.

A derrick can be a structure used to support a crown block and atraveling block operatively coupled to the crown block at least in partvia line. A derrick may be pyramidal in shape and offer a suitablestrength-to-weight ratio. A derrick may be movable as a unit or in apiece by piece manner (e.g., to be assembled and disassembled).

As an example, drawworks may include a spool, brakes, a power source andassorted auxiliary devices. Drawworks may controllably reel out and reelin line. Line may be reeled over a crown block and coupled to atraveling block to gain mechanical advantage in a “block and tackle” or“pulley” fashion. Reeling out and in of line can cause a traveling block(e.g., and whatever may be hanging underneath it), to be lowered into orraised out of a bore. Reeling out of line may be powered by gravity andreeling in by a motor, an engine, etc. (e.g., an electric motor, adiesel engine, etc.).

As an example, a crown block can include a set of pulleys (e.g.,sheaves) that can be located at or near a top of a derrick or a mast,over which line is threaded. A traveling block can include a set ofsheaves that can be moved up and down in a derrick or a mast via linethreaded in the set of sheaves of the traveling block and in the set ofsheaves of a crown block. A crown block, a traveling block and a linecan form a pulley system of a derrick or a mast, which may enablehandling of heavy loads (e.g., drillstring, pipe, casing, liners, etc.)to be lifted out of or lowered into a bore. As an example, line may beabout a centimeter to about five centimeters in diameter as, forexample, steel cable. Through use of a set of sheaves, such line maycarry loads heavier than the line could support as a single strand.

As an example, a derrick person may be a rig crew member that works on aplatform attached to a derrick or a mast. A derrick can include alanding on which a derrick person may stand. As an example, such alanding may be about 10 meters or more above a rig floor. In anoperation referred to as trip out of the hole (TOH), a derrick personmay wear a safety harness that enables leaning out from the work landing(e.g., monkeyboard) to reach pipe in located at or near the center of aderrick or a mast and to throw a line around the pipe and pull it backinto its storage location (e.g., fingerboards), for example, until it atime at which it may be desirable to run the pipe back into the bore. Asan example, a rig may include automated pipe-handling equipment suchthat the derrick person controls the machinery rather than physicallyhandling the pipe.

As an example, a trip may refer to the act of pulling equipment from abore and/or placing equipment in a bore. As an example, equipment mayinclude a drillstring that can be pulled out of the hole and/or place orreplaced in the hole. As an example, a pipe trip may be performed wherea drill bit has dulled or has otherwise ceased to drill efficiently andis to be replaced.

FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsitethat may be onshore or offshore). As shown, the wellsite system 200 caninclude a mud tank 201 for holding mud and other material (e.g., wheremud can be a drilling fluid), a suction line 203 that serves as an inletto a mud pump 204 for pumping mud from the mud tank 201 such that mudflows to a vibrating hose 206, a drawworks 207 for winching drill lineor drill lines 212, a standpipe 208 that receives mud from the vibratinghose 206, a kelly hose 209 that receives mud from the standpipe 208, agooseneck or goosenecks 210, a traveling block 211, a crown block 213for carrying the traveling block 211 via the drill line or drill lines212 (see, e.g., the crown block 173 of FIG. 1), a derrick 214 (see,e.g., the derrick 172 of FIG. 1), a kelly 218 or a top drive 240, akelly drive bushing 219, a rotary table 220, a drill floor 221, a bellnipple 222, one or more blowout preventors (BOPs) 223, a drillstring225, a drill bit 226, a casing head 227 and a flow pipe 228 that carriesmud and other material to, for example, the mud tank 201.

In the example system of FIG. 2, a borehole 232 is formed in subsurfaceformations 230 by rotary drilling; noting that various exampleembodiments may also use directional drilling.

As shown in the example of FIG. 2, the drillstring 225 is suspendedwithin the borehole 232 and has a drillstring assembly 250 that includesthe drill bit 226 at its lower end. As an example, the drillstringassembly 250 may be a bottom hole assembly (BHA).

The wellsite system 200 can provide for operation of the drillstring 225and other operations. As shown, the wellsite system 200 includes theplatform and the derrick 214 positioned over the borehole 232. Asmentioned, the wellsite system 200 can include the rotary table 220where the drillstring 225 pass through an opening in the rotary table220.

As shown in the example of FIG. 2, the wellsite system 200 can includethe kelly 218 and associated components, etc., or a top drive 240 andassociated components. As to a kelly example, the kelly 218 may be asquare or hexagonal metal/alloy bar with a hole drilled therein thatserves as a mud flow path. The kelly 218 can be used to transmit rotarymotion from the rotary table 220 via the kelly drive bushing 219 to thedrillstring 225, while allowing the drillstring 225 to be lowered orraised during rotation. The kelly 218 can pass through the kelly drivebushing 219, which can be driven by the rotary table 220. As an example,the rotary table 220 can include a master bushing that operativelycouples to the kelly drive bushing 219 such that rotation of the rotarytable 220 can turn the kelly drive bushing 219 and hence the kelly 218.The kelly drive bushing 219 can include an inside profile matching anoutside profile (e.g., square, hexagonal, etc.) of the kelly 218;however, with slightly larger dimensions so that the kelly 218 canfreely move up and down inside the kelly drive bushing 219.

As to a top drive example, the top drive 240 can provide functionsperformed by a kelly and a rotary table. The top drive 240 can turn thedrillstring 225. As an example, the top drive 240 can include one ormore motors (e.g., electric and/or hydraulic) connected with appropriategearing to a short section of pipe called a quill, that in turn may bescrewed into a saver sub or the drillstring 225 itself. The top drive240 can be suspended from the traveling block 211, so the rotarymechanism is free to travel up and down the derrick 214. As an example,a top drive 240 may allow for drilling to be performed with more jointstands than a kelly/rotary table approach.

In the example of FIG. 2, the mud tank 201 can hold mud, which can beone or more types of drilling fluids. As an example, a wellbore may bedrilled to produce fluid, inject fluid or both (e.g., hydrocarbons,minerals, water, etc.).

In the example of FIG. 2, the drillstring 225 (e.g., including one ormore downhole tools) may be composed of a series of pipes threadablyconnected together to form a long tube with the drill bit 226 at thelower end thereof. As the drillstring 225 is advanced into a wellborefor drilling, at some point in time prior to or coincident withdrilling, the mud may be pumped by the pump 204 from the mud tank 201(e.g., or other source) via a the lines 206, 208 and 209 to a port ofthe kelly 218 or, for example, to a port of the top drive 240. The mudcan then flow via a passage (e.g., or passages) in the drillstring 225and out of ports located on the drill bit 226 (see, e.g., a directionalarrow). As the mud exits the drillstring 225 via ports in the drill bit226, it can then circulate upwardly through an annular region between anouter surface(s) of the drillstring 225 and surrounding wall(s) (e.g.,open borehole, casing, etc.), as indicated by directional arrows. Insuch a manner, the mud lubricates the drill bit 226 and carries heatenergy (e.g., frictional or other energy) and formation cuttings to thesurface where the mud (e.g., and cuttings) may be returned to the mudtank 201, for example, for recirculation (e.g., with processing toremove cuttings, etc.).

The mud pumped by the pump 204 into the drillstring 225 may, afterexiting the drillstring 225, form a mudcake that lines the wellborewhich, among other functions, may reduce friction between thedrillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.).A reduction in friction may facilitate advancing or retracting thedrillstring 225. During a drilling operation, the entire drill string225 may be pulled from a wellbore and optionally replaced, for example,with a new or sharpened drill bit, a smaller diameter drill string, etc.As mentioned, the act of pulling a drill string out of a hole orreplacing it in a hole is referred to as tripping. A trip may bereferred to as an upward trip or an outward trip or as a downward tripor an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drillbit 226 of the drill string 225 at a bottom of a wellbore, pumping ofthe mud commences to lubricate the drill bit 226 for purposes ofdrilling to enlarge the wellbore. As mentioned, the mud can be pumped bythe pump 204 into a passage of the drillstring 225 and, upon filling ofthe passage, the mud may be used as a transmission medium to transmitenergy, for example, energy that may encode information as in mud-pulsetelemetry.

As an example, mud-pulse telemetry equipment may include a downholedevice configured to effect changes in pressure in the mud to create anacoustic wave or waves upon which information may modulated. In such anexample, information from downhole equipment (e.g., one or more modulesof the drillstring 225) may be transmitted uphole to an uphole device,which may relay such information to other equipment for processing,control, etc.

As an example, telemetry equipment may operate via transmission ofenergy via the drillstring 225 itself. For example, consider a signalgenerator that imparts coded energy signals to the drillstring 225 andrepeaters that may receive such energy and repeat it to further transmitthe coded energy signals (e.g., information, etc.).

As an example, the drillstring 225 may be fitted with telemetryequipment 252 that includes a rotatable drive shaft, a turbine impellermechanically coupled to the drive shaft such that the mud can cause theturbine impeller to rotate, a modulator rotor mechanically coupled tothe drive shaft such that rotation of the turbine impeller causes saidmodulator rotor to rotate, a modulator stator mounted adjacent to orproximate to the modulator rotor such that rotation of the modulatorrotor relative to the modulator stator creates pressure pulses in themud, and a controllable brake for selectively braking rotation of themodulator rotor to modulate pressure pulses. In such example, analternator may be coupled to the aforementioned drive shaft where thealternator includes at least one stator winding electrically coupled toa control circuit to selectively short the at least one stator windingto electromagnetically brake the alternator and thereby selectivelybrake rotation of the modulator rotor to modulate the pressure pulses inthe mud.

In the example of FIG. 2, an uphole control and/or data acquisitionsystem 262 may include circuitry to sense pressure pulses generated bytelemetry equipment 252 and, for example, communicate sensed pressurepulses or information derived therefrom for process, control, etc.

The assembly 250 of the illustrated example includes alogging-while-drilling (LWD) module 254, a measuring-while-drilling(MWD) module 256, an optional module 258, a roto-steerable system andmotor 260, and the drill bit 226.

The LWD module 254 may be housed in a suitable type of drill collar andcan contain one or a plurality of selected types of logging tools. Itwill also be understood that more than one LWD and/or MWD module can beemployed, for example, as represented at by the module 256 of thedrillstring assembly 250. Where the position of an LWD module ismentioned, as an example, it may refer to a module at the position ofthe LWD module 254, the module 256, etc. An LWD module can includecapabilities for measuring, processing, and storing information, as wellas for communicating with the surface equipment. In the illustratedexample, the LWD module 254 may include a seismic measuring device.

The MWD module 256 may be housed in a suitable type of drill collar andcan contain one or more devices for measuring characteristics of thedrillstring 225 and the drill bit 226. As an example, the MWD tool 254may include equipment for generating electrical power, for example, topower various components of the drillstring 225. As an example, the MWDtool 254 may include the telemetry equipment 252, for example, where theturbine impeller can generate power by flow of the mud; it beingunderstood that other power and/or battery systems may be employed forpurposes of powering various components. As an example, the MWD module256 may include one or more of the following types of measuring devices:a weight-on-bit measuring device, a torque measuring device, a vibrationmeasuring device, a shock measuring device, a stick slip measuringdevice, a direction measuring device, and an inclination measuringdevice.

FIG. 2 also shows some examples of types of holes that may be drilled.For example, consider a slant hole 272, an S-shaped hole 274, a deepinclined hole 276 and a horizontal hole 278.

As an example, a drilling operation can include directional drillingwhere, for example, at least a portion of a well includes a curved axis.For example, consider a radius that defines curvature where aninclination with regard to the vertical may vary until reaching an anglebetween about 30 degrees and about 60 degrees or, for example, an angleto about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where eachof the shapes may aim to meet particular operational demands. As anexample, a drilling process may be performed on the basis of informationas and when it is relayed to a drilling engineer. As an example,inclination and/or direction may be modified based on informationreceived during a drilling process.

As an example, deviation of a bore may be accomplished in part by use ofa downhole motor and/or a turbine. As to a motor, for example, adrillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipmentto perform method such as geosteering. As an example, a steerable systemcan include a PDM or of a turbine on a lower part of a drillstringwhich, just above a drill bit, a bent sub can be mounted. As an example,above a PDM, MWD equipment that provides real time or near real timedata of interest (e.g., inclination, direction, pressure, temperature,real weight on the drill bit, torque stress, etc.) and/or LWD equipmentmay be installed. As to the latter, LWD equipment can make it possibleto send to the surface various types of data of interest, including forexample, geological data (e.g., gamma ray log, resistivity, density andsonic logs, etc.).

The coupling of sensors providing information on the course of a welltrajectory, in real time or near real time, with, for example, one ormore logs characterizing the formations from a geological viewpoint, canallow for implementing a geosteering method. Such a method can includenavigating a subsurface environment, for example, to follow a desiredroute to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron(AND) tool for measuring density and porosity; a MWD tool for measuringinclination, azimuth and shocks; a compensated dual resistivity (CDR)tool for measuring resistivity and gamma ray related phenomena; one ormore variable gauge stabilizers; one or more bend joints; and ageosteering tool, which may include a motor and optionally equipment formeasuring and/or responding to one or more of inclination, resistivityand gamma ray related phenomena.

As an example, geosteering can include intentional directional controlof a wellbore based on results of downhole geological loggingmeasurements in a manner that aims to keep a directional wellbore withina desired region, zone (e.g., a pay zone), etc. As an example,geosteering may include directing a wellbore to keep the wellbore in aparticular section of a reservoir, for example, to minimize gas and/orwater breakthrough and, for example, to maximize economic productionfrom a well that includes the wellbore.

Referring again to FIG. 2, the wellsite system 200 can include one ormore sensors 264 that are operatively coupled to the control and/or dataacquisition system 262. As an example, a sensor or sensors may be atsurface locations. As an example, a sensor or sensors may be at downholelocations. As an example, a sensor or sensors may be at one or moreremote locations that are not within a distance of the order of aboutone hundred meters from the wellsite system 200. As an example, a sensoror sensor may be at an offset wellsite where the wellsite system 200 andthe offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 264 can be provided fortracking pipe, tracking movement of at least a portion of a drillstring,etc.

As an example, the system 200 can include one or more sensors 266 thatcan sense and/or transmit signals to a fluid conduit such as a drillingfluid conduit (e.g., a drilling mud conduit). For example, in the system200, the one or more sensors 266 can be operatively coupled to portionsof the standpipe 208 through which mud flows. As an example, a downholetool can generate pulses that can travel through the mud and be sensedby one or more of the one or more sensors 266. In such an example, thedownhole tool can include associated circuitry such as, for example,encoding circuitry that can encode signals, for example, to reducedemands as to transmission. As an example, circuitry at the surface mayinclude decoding circuitry to decode encoded information transmitted atleast in part via mud-pulse telemetry. As an example, circuitry at thesurface may include encoder circuitry and/or decoder circuitry andcircuitry downhole may include encoder circuitry and/or decodercircuitry. As an example, the system 200 can include a transmitter thatcan generate signals that can be transmitted downhole via mud (e.g.,drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck.The term stuck can refer to one or more of varying degrees of inabilityto move or remove a drillstring from a bore. As an example, in a stuckcondition, it might be possible to rotate pipe or lower it back into abore or, for example, in a stuck condition, there may be an inability tomove the drillstring axially in the bore, though some amount of rotationmay be possible. As an example, in a stuck condition, there may be aninability to move at least a portion of the drillstring axially androtationally.

As to the term “stuck pipe”, the can refer to a portion of a drillstringthat cannot be rotated or moved axially. As an example, a conditionreferred to as “differential sticking” can be a condition whereby thedrillstring cannot be moved (e.g., rotated or reciprocated) along theaxis of the bore. Differential sticking may occur when high-contactforces caused by low reservoir pressures, high wellbore pressures, orboth, are exerted over a sufficiently large area of the drillstring.Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differentialpressure between the wellbore and the reservoir and the area that thedifferential pressure is acting upon. This means that a relatively lowdifferential pressure (delta p) applied over a large working area can bejust as effective in sticking pipe as can a high differential pressureapplied over a small area.

As an example, a condition referred to as “mechanical sticking” can be acondition where limiting or prevention of motion of the drillstring by amechanism other than differential pressure sticking occurs. Mechanicalsticking can be caused, for example, by one or more of junk in the hole,wellbore geometry anomalies, cement, keyseats or a buildup of cuttingsin the annulus.

FIG. 3 shows an example of a system 300 that includes various equipmentfor evaluation 310, planning 320, engineering 330 and operations 340.For example, a drilling workflow framework 301, a seismic-to-simulationframework 302, a technical data framework 303 and a drilling framework304 may be implemented to perform one or more processes such as aevaluating a formation 314, evaluating a process 318, generating atrajectory 324, validating a trajectory 328, formulating constraints334, designing equipment and/or processes based at least in part onconstraints 338, performing drilling 344 and evaluating drilling and/orformation 348.

In the example of FIG. 3, the seismic-to-simulation framework 302 canbe, for example, the PETREL® framework (Schlumberger Limited, Houston,Tex.) and the technical data framework 303 can be, for example, theTECHLOG® framework (Schlumberger Limited, Houston, Tex.).

As an example, a framework can include entities that may include earthentities, geological objects or other objects such as wells, surfaces,reservoirs, etc. Entities can include virtual representations of actualphysical entities that are reconstructed for purposes of one or more ofevaluation, planning, engineering, operations, etc.

Entities may include entities based on data acquired via sensing,observation, etc. (e.g., seismic data and/or other information). Anentity may be characterized by one or more properties (e.g., ageometrical pillar grid entity of an earth model may be characterized bya porosity property). Such properties may represent one or moremeasurements (e.g., acquired data), calculations, etc.

A framework may be an object-based framework. In such a framework,entities may include entities based on pre-defined classes, for example,to facilitate modeling, analysis, simulation, etc. An example of anobject-based framework is the MICROSOFT™ .NET™ framework (Redmond,Wash.), which provides a set of extensible object classes. In the .NET™framework, an object class encapsulates a module of reusable code andassociated data structures. Object classes can be used to instantiateobject instances for use in by a program, script, etc. For example,borehole classes may define objects for representing boreholes based onwell data.

As an example, a framework can include an analysis component that mayallow for interaction with a model or model-based results (e.g.,simulation results, etc.). As to simulation, a framework may operativelylink to or include a simulator such as the ECLIPSE® reservoir simulator(Schlumberger Limited, Houston Tex.), the INTERSECT® reservoir simulator(Schlumberger Limited, Houston Tex.), etc.

The aforementioned PETREL® framework provides components that allow foroptimization of exploration and development operations. The PETREL®framework includes seismic to simulation software components that canoutput information for use in increasing reservoir performance, forexample, by improving asset team productivity. Through use of such aframework, various professionals (e.g., geophysicists, geologists, wellengineers, reservoir engineers, etc.) can develop collaborativeworkflows and integrate operations to streamline processes. Such aframework may be considered an application and may be considered adata-driven application (e.g., where data is input for purposes ofmodeling, simulating, etc.).

As an example, one or more frameworks may be interoperative and/or runupon one or another. As an example, consider the framework environmentmarketed as the OCEAN® framework environment (Schlumberger Limited,Houston, Tex.), which allows for integration of add-ons (or plug-ins)into a PETREL® framework workflow. The OCEAN® framework environment canleverage .NET™ tools (Microsoft Corporation, Redmond, Wash.) and offersstable, user-friendly interfaces for efficient development. In anexample embodiment, various components may be implemented as add-ons (orplug-ins) that conform to and operate according to specifications of aframework environment (e.g., according to application programminginterface (API) specifications, etc.).

As an example, a framework can include a model simulation layer alongwith a framework services layer, a framework core layer and a moduleslayer. The framework may include the OCEAN® framework where the modelsimulation layer can include or operatively link to the PETREL®model-centric software package that hosts OCEAN® framework applications.In an example embodiment, the PETREL® software may be considered adata-driven application. The PETREL® software can include a frameworkfor model building and visualization. Such a model may include one ormore grids.

As an example, the model simulation layer may provide domain objects,act as a data source, provide for rendering and provide for various userinterfaces. Rendering may provide a graphical environment in whichapplications can display their data while the user interfaces mayprovide a common look and feel for application user interfacecomponents.

As an example, domain objects can include entity objects, propertyobjects and optionally other objects. Entity objects may be used togeometrically represent wells, surfaces, reservoirs, etc., whileproperty objects may be used to provide property values as well as dataversions and display parameters. For example, an entity object mayrepresent a well where a property object provides log information aswell as version information and display information (e.g., to displaythe well as part of a model).

As an example, data may be stored in one or more data sources (or datastores, generally physical data storage devices), which may be at thesame or different physical sites and accessible via one or morenetworks. As an example, a model simulation layer may be configured tomodel projects. As such, a particular project may be stored where storedproject information may include inputs, models, results and cases. Thus,upon completion of a modeling session, a user may store a project. At alater time, the project can be accessed and restored using the modelsimulation layer, which can recreate instances of the relevant domainobjects.

As an example, the system 300 may be used to perform one or moreworkflows. A workflow may be a process that includes a number ofworksteps. A workstep may operate on data, for example, to create newdata, to update existing data, etc. As an example, a workflow mayoperate on one or more inputs and create one or more results, forexample, based on one or more algorithms. As an example, a system mayinclude a workflow editor for creation, editing, executing, etc. of aworkflow. In such an example, the workflow editor may provide forselection of one or more pre-defined worksteps, one or more customizedworksteps, etc. As an example, a workflow may be a workflowimplementable at least in part in the PETREL® software, for example,that operates on seismic data, seismic attribute(s), etc. As an example,a workflow can include generating one or more control signals andissuing one or more control signals to one or more pieces of equipmentthat can perform one or more actions in a field operation.

As an example, seismic data can be data acquired via a seismic surveywhere sources and receivers are positioned in a geologic environment toemit and receive seismic energy where at least a portion of such energycan reflect off subsurface structures. As an example, a seismic dataanalysis framework or frameworks (e.g., consider the OMEGA® framework,marketed by Schlumberger Limited, Houston, Tex.) may be utilized todetermine depth, extent, properties, etc. of subsurface structures. Asan example, seismic data analysis can include forward modeling and/orinversion, for example, to iteratively build a model of a subsurfaceregion of a geologic environment. As an example, a seismic data analysisframework may be part of or operatively coupled to aseismic-to-simulation framework (e.g., the PETREL® framework, etc.).

As an example, a workflow may be a process implementable at least inpart in the OCEAN® framework. As an example, a workflow may include oneor more worksteps that access a module such as a plug-in (e.g., externalexecutable code, etc.).

As an example, a framework may provide for modeling petroleum systems.For example, the modeling framework marketed as the PETROMOD® framework(Schlumberger Limited, Houston, Tex.) includes features for input ofvarious types of information (e.g., seismic, well, geological, etc.) tomodel evolution of a sedimentary basin. The PETROMOD® framework providesfor petroleum systems modeling via input of various data such as seismicdata, well data and other geological data, for example, to modelevolution of a sedimentary basin. The PETROMOD® framework may predictif, and how, a reservoir has been charged with hydrocarbons, including,for example, the source and timing of hydrocarbon generation, migrationroutes, quantities, pore pressure and hydrocarbon type in the subsurfaceor at surface conditions. In combination with a framework such as thePETREL® framework, workflows may be constructed to providebasin-to-prospect scale exploration solutions. Data exchange betweenframeworks can facilitate construction of models, analysis of data(e.g., PETROMOD® framework data analyzed using PETREL® frameworkcapabilities), and coupling of workflows.

As mentioned, a drillstring can include various tools that may makemeasurements. As an example, a wireline tool or another type of tool maybe utilized to make measurements. As an example, a tool may beconfigured to acquire electrical borehole images. As an example, thefullbore Formation MicroImager (FMI) tool (Schlumberger Limited,Houston, Tex.) can acquire borehole image data. A data acquisitionsequence for such a tool can include running the tool into a boreholewith acquisition pads closed, opening and pressing the pads against awall of the borehole, delivering electrical current into the materialdefining the borehole while translating the tool in the borehole, andsensing current remotely, which is altered by interactions with thematerial.

Analysis of formation information may reveal features such as, forexample, vugs, dissolution planes (e.g., dissolution along beddingplanes), stress-related features, dip events, etc. As an example, a toolmay acquire information that may help to characterize a reservoir,optionally a fractured reservoir where fractures may be natural and/orartificial (e.g., hydraulic fractures). As an example, informationacquired by a tool or tools may be analyzed using a framework such asthe TECHLOG® framework. As an example, the TECHLOG® framework can beinteroperable with one or more other frameworks such as, for example,the PETREL® framework.

FIG. 4 shows an example of a system 400 that includes a client layer410, an applications layer 440 and a storage layer 460. As shown theclient layer 410 can be in communication with the applications layer 440and the applications layer 440 can be in communication with the storagelayer 460.

The client layer 410 can include features that allow for access andinteractions via one or more private networks 412, one or more mobileplatforms and/or mobile networks 414 and via the “cloud” 416, which maybe considered to include distributed equipment that forms a network suchas a network of networks.

In the example of FIG. 4, the applications layer 440 includes thedrilling workflow framework 301 as mentioned with respect to the exampleof FIG. 3. The applications layer 440 also includes a databasemanagement component 442 that includes one or more search enginesmodules.

As an example, the database management component 442 can include one ormore search engine modules that provide for searching one or moreinformation that may be stored in one or more data repositories. As anexample, the STUDIO E&P™ knowledge environment (Schlumberger Ltd.,Houston, Tex.) includes STUDIO FIND™ search functionality, whichprovides a search engine. The STUDIO FIND™ search functionality alsoprovides for indexing content, for example, to create one or moreindexes. As an example, search functionality may provide for access topublic content, private content or both, which may exist in one or moredatabases, for example, optionally distributed and accessible via anintranet, the Internet or one or more other networks. As an example, asearch engine may be configured to apply one or more filters from a setor sets of filters, for example, to enable users to filter out data thatmay not be of interest.

As an example, a framework may provide for interaction with a searchengine and, for example, associated features such as features of theSTUDIO FIND™ search functionality. As an example, a framework mayprovide for implementation of one or more spatial filters (e.g., basedon an area viewed on a display, static data, etc.). As an example, asearch may provide access to dynamic data (e.g., “live” data from one ormore sources), which may be available via one or more networks (e.g.,wired, wireless, etc.). As an example, one or more modules mayoptionally be implemented within a framework or, for example, in amanner operatively coupled to a framework (e.g., as an add-on, aplug-in, etc.). As an example, a module for structuring search results(e.g., in a list, a hierarchical tree structure, etc.) may optionally beimplemented within a framework or, for example, in a manner operativelycoupled to a framework (e.g., as an add-on, a plug-in, etc.).

In the example of FIG. 4, the applications layer 440 can includecommunicating with one or more resources such as, for example, theseismic-to-simulation framework 302, the drilling framework 304 and/orone or more sites, which may be or include one or more offset wellsites.As an example, the applications layer 440 may be implemented for aparticular wellsite where information can be processed as part of aworkflow for operations such as, for example, operations performed,being performed and/or to be performed at the particular wellsite. As anexample, an operation may involve directional drilling, for example, viageosteering.

In the example of FIG. 4, the storage layer 460 can include varioustypes of data, information, etc., which may be stored in one or moredatabases 462. As an example, one or more servers 464 may provide formanagement, access, etc., to data, information, etc., stored in the oneor more databases 462. As an example, the module 442 may provide forsearching as to data, information, etc., stored in the one or moredatabases 462.

As an example, the module 442 may include features for indexing, etc. Asan example, information may be indexed at least in part with respect towellsite. For example, where the applications layer 440 is implementedto perform one or more workflows associated with a particular wellsite,data, information, etc., associated with that particular wellsite may beindexed based at least in part on the wellsite being an index parameter(e.g., a search parameter).

As an example, the system 400 of FIG. 4 may be implemented to performone or more portions of one or more workflows associated with the system300 of FIG. 3. For example, the drilling workflow framework 301 mayinteract with the technical data framework 303 and the drillingframework 304 before, during and/or after performance of one or moredrilling operations. In such an example, the one or more drillingoperations may be performed in a geologic environment (see, e.g., theenvironment 150 of FIG. 1) using one or more types of equipment (see,e.g., equipment of FIGS. 1 and 2).

FIG. 5 shows an example of a system 500 that includes a computing device501, an application services block 510, a bootstrap services block 520,a cloud gateway block 530, a cloud portal block 540, a stream processingservices block 550, one or more databases 560, a management servicesblock 570 and a service systems manager 590.

In the example of FIG. 5, the computing device 501 can include one ormore processors 502, memory 503, one or more interfaces 504 and locationcircuitry 505 or, for example, one of the one or more interfaces 504 maybe operatively coupled to location circuitry that can acquire locallocation information. For example, the computing device 501 can includeGPS circuitry as location circuitry such that the approximate locationof the computer device 501 can be determined. While GPS is mentioned(Global Positioning System), location circuitry may employ one or moretypes of locating techniques. For example, consider one or more ofGLONASS, GALILEO, BeiDou-2, or another system (e.g., global navigationsatellite system, “GNSS”). As an example, location circuitry may includecellular phone circuitry (e.g., LTE, 3G, 4G, etc.). As an example,location circuitry may include WiFi circuitry.

As an example, the application services block 510 can be implemented viainstructions executable using the computing device 501. As an example,the computing device 501 may be at a wellsite and part of wellsiteequipment. As an example, the computing device 501 may be a mobilecomputing device (e.g., tablet, laptop, etc.) or a desktop computingdevice that may be mobile, for example, as part of wellsite equipment(e.g., doghouse equipment, rig equipment, vehicle equipment, etc.).

As an example, the system 500 can include performing various actions.For example, the system 500 may include a token that is utilized as asecurity measure to assure that information (e.g., data) is associatedwith appropriate permission or permissions for transmission, storage,access, etc.

In the example of FIG. 5, various circles are shown with labels A to H.As an example, A can be a process where an administrator creates ashared access policy (e.g., manually, via an API, etc.); B can be aprocess for allocating a shared access key for a device identifier(e.g., a device ID), which may be performed manually, via an API, etc.);C can be a process for creating a “device” that can be registered in adevice registry and for allocating a symmetric key; D can be a processfor persisting metadata where such metadata may be associated with awellsite identifier (e.g., a well ID) and where, for example, locationinformation (e.g., GPS based information, etc.) may be associated with adevice ID and a well ID; E can be a process where a bootstrap messagepasses that includes a device ID (e.g., a trusted platform module (TPM)chip ID that may be embedded within a device) and that includes a wellID and location information such that bootstrap services (e.g., of thebootstrap services block 520) can proceed to obtain shared accesssignature (SAS) key(s) to a cloud service endpoint for authorization; Fcan be a process for provisioning a device, for example, if not alreadyprovisioned, where, for example, the process can include returningdevice keys and endpoint; G can be a process for getting a SAS tokenusing an identifier and a key; and H can be a process that includesbeing ready to send a message using device credentials. Also shown inFIG. 5 is a process for getting a token and issuing a command for a wellidentifier (see label Z).

As an example, Shared Access Signatures can be an authenticationmechanism based on, for example, SHA-256 secure hashes, URIs, etc. As anexample, SAS may be used by one or more Service Bus services. SAS can beimplemented via a Shared Access Policy and a Shared Access Signature,which may be referred to as a token. As an example, for SAS applicationsusing the AZURE™ .NET™ SDK with the Service Bus, .NET™ libraries can useSAS authorization through the SharedAccessSignatureTokenProvider class.

As an example, where a system gives an entity (e.g., a sender, a client,etc.) a SAS token, that entity does not have the key directly, and thatentity cannot reverse the hash to obtain it. As such, there is controlover what that entity can access and, for example, for how long accessmay exist. As an example, in SAS, for a change of the primary key in thepolicy, Shared Access Signatures created from it will be invalidated.

As an example, the system 500 of FIG. 5 can be implemented forprovisioning of rig acquisition system and/or data delivery.

As an example, a method can include establishing an Internet of Things(IoT) hub or hubs. As an example, such a hub or hubs can include one ormore device registries. In such an example, the hub or hubs may providefor storage of metadata associated with a device and, for example, aper-device authentication model. As an example, where locationinformation indicates that a device (e.g., wellsite equipment, etc.) hasbeen changed with respect to its location, a method can include revokingthe device in a hub.

As an example, such an architecture utilized in a system such as, forexample, the system 500, may include features of the AZURE™ architecture(Microsoft Corporation, Redmond, Wash.) and/or one or more other cloudarchitectures. As an example, the cloud portal block 540 can include oneor more features of an AZURE™ portal that can manage, mediate, etc.access to one or more services, data, connections, networks, devices,etc.

As an example, the system 500 can include a cloud computing platform andinfrastructure, for example, for building, deploying, and managingapplications and services (e.g., through a network of datacenters,etc.). As an example, such a cloud platform may provide PaaS and IaaSservices and support one or more different programming languages, toolsand frameworks, etc.

FIG. 6 shows an example of a system 600 associated with an example of awellsite system 601 and also shows an example scenario 602. As shown inFIG. 6, the system 600 can include a front-end 603 and a back-end 605from an outside or external perspective (e.g., external to the wellsitesystem 601, etc.). In the example of FIG. 6, the system 600 includes adrilling framework 620, a stream processing and/or management block 640,storage 660 and optionally one or more other features that can bedefined as being back-end features. In the example of FIG. 6, the system600 includes a drilling workflow framework 610, a stream processingand/or management block 630, applications 650 and optionally one or moreother features that can be defined as being front-end features.

As an example, a user operating a user device can interact with thefront-end 603 where the front-end 603 can interact with one or morefeatures of the back-end 605. As an example, such interactions may beimplemented via one or more networks, which may be associated with acloud platform (e.g., cloud resources, etc.).

As to the example scenario 602, the drilling framework 620 can provideinformation associated with, for example, the wellsite system 601. Asshown, the stream blocks 630 and 640, a query service 685 and thedrilling workflow framework 610 may receive information and direct suchinformation to storage, which may include a time series database 662, ablob storage database 664, a document database 666, a well informationdatabase 668, a project(s) database 669, etc. As an example, the wellinformation database 668 may receive and store information such as, forexample, customer information (e.g., from entities that may be owners ofrights at a wellsite, service providers at a wellsite, etc.). As anexample, the project database 669 can include information from aplurality of projects where a project may be, for example, a wellsiteproject.

As an example, the system 600 can be operable for a plurality ofwellsites, which may include active and/or inactive wellsites and/or,for example, one or more planned wellsites. As an example, the system600 can include various components of the system 300 of FIG. 3. As anexample, the system 600 can include various components of the system 400of FIG. 4. For example, the drilling workflow framework 610 can be adrilling workflow framework such as the drilling workflow framework 301and/or, for example, the drilling framework 620 can be a drillingframework such as the drilling framework 304.

FIG. 7 shows an example of a wellsite system 700, specifically, FIG. 7shows the wellsite system 700 in an approximate side view and anapproximate plan view along with a block diagram of a system 770.

In the example of FIG. 7, the wellsite system 700 can include a cabin710, a rotary table 722, drawworks 724, a mast 726 (e.g., optionallycarrying a top drive, etc.), mud tanks 730 (e.g., with one or morepumps, one or more shakers, etc.), one or more pump buildings 740, aboiler building 742, an HPU building 744 (e.g., with a rig fuel tank,etc.), a combination building 748 (e.g., with one or more generators,etc.), pipe tubs 762, a catwalk 764, a flare 768, etc. Such equipmentcan include one or more associated functions and/or one or moreassociated operational risks, which may be risks as to time, resources,and/or humans.

As shown in the example of FIG. 7, the wellsite system 700 can include asystem 770 that includes one or more processors 772, memory 774operatively coupled to at least one of the one or more processors 772,instructions 776 that can be, for example, stored in the memory 774, andone or more interfaces 778. As an example, the system 770 can includeone or more processor-readable media that include processor-executableinstructions executable by at least one of the one or more processors772 to cause the system 770 to control one or more aspects of thewellsite system 700. In such an example, the memory 774 can be orinclude the one or more processor-readable media where theprocessor-executable instructions can be or include instructions. As anexample, a processor-readable medium can be a computer-readable storagemedium that is not a signal and that is not a carrier wave.

FIG. 7 also shows a battery 780 that may be operatively coupled to thesystem 770, for example, to power the system 770. As an example, thebattery 780 may be a back-up battery that operates when another powersupply is unavailable for powering the system 770. As an example, thebattery 780 may be operatively coupled to a network, which may be acloud network. As an example, the battery 780 can include smart batterycircuitry and may be operatively coupled to one or more pieces ofequipment via a SMBus or other type of bus.

In the example of FIG. 7, services 790 are shown as being available, forexample, via a cloud platform. Such services can include data services792, query services 794 and drilling services 796. As an example, theservices 790 may be part of a system such as the system 300 of FIG. 3,the system 400 of FIG. 4 and/or the system 600 of FIG. 6.

As an example, a system such as, for example, the system 300 of FIG. 3may be utilized to perform a workflow. Such a system may be distributedand allow for collaborative workflow interactions and may be consideredto be a platform (e.g., a framework for collaborative interactions,etc.).

As an example, one or more systems can be utilized to implement aworkflow that can be performed collaboratively. As an example, thesystem 300 of FIG. 3 can be operated as a distributed, collaborativewell-planning system. The system 300 can utilize one or more servers,one or more client devices, etc. and may maintain one or more databases,data files, etc., which may be accessed and modified by one or moreclient devices, for example, using a web browser, remote terminal, etc.As an example, a client device may modify a database or data fileson-the-fly, and/or may include “sandboxes” that may permit one or moreclient devices to modify at least a portion of a database or data filesoptionally off-line, for example, without affecting a database or datafiles seen by one or more other client devices. As an example, a clientdevice that includes a sandbox may modify a database or data file aftercompleting an activity in the sandbox.

In some examples, client devices and/or servers may be remote withrespect to one another and/or may individually include two or moreremote processing units. As an example, two systems can be “remote” withrespect to one another if they are not physically proximate to oneanother; for example, two devices that are located at different sides ofa room, in different rooms, in different buildings, in different cities,countries, etc. may be considered “remote” depending on the context. Insome embodiments, two or more client devices may be proximate to oneanother, and/or one or more client devices and a server may be proximateto one another.

As an example, various aspects of a workflow may be completedautomatically, may be partially automated, or may be completed manually,as by a human user interfacing with a software application. As anexample, a workflow may be cyclic, and may include, as an example, fourstages such as, for example, an evaluation stage (see, e.g., theevaluation equipment 310), a planning stage (see, e.g., the planningequipment 320), an engineering stage (see, e.g., the engineeringequipment 330) and an execution stage (see, e.g., the operationsequipment 340). As an example, a workflow may commence at one or morestages, which may progress to one or more other stages (e.g., in aserial manner, in a parallel manner, in a cyclical manner, etc.).

As an example, a workflow can commence with an evaluation stage, whichmay include a geological service provider evaluating a formation (see,e.g., the evaluation block 314). As an example, a geological serviceprovider may undertake the formation evaluation using a computing systemexecuting a software package tailored to such activity; or, for example,one or more other suitable geology platforms may be employed (e.g.,alternatively or additionally). As an example, the geological serviceprovider may evaluate the formation, for example, using earth models,geophysical models, basin models, petrotechnical models, combinationsthereof, and/or the like. Such models may take into consideration avariety of different inputs, including offset well data, seismic data,pilot well data, other geologic data, etc. The models and/or the inputmay be stored in the database maintained by the server and accessed bythe geological service provider.

As an example, a workflow may progress to a geology and geophysics(“G&G”) service provider, which may generate a well trajectory (see,e.g., the generation block 324), which may involve execution of one ormore G&G software packages. Examples of such software packages includethe PETREL® framework. As an example, a G&G service provider maydetermine a well trajectory or a section thereof, based on, for example,one or more model(s) provided by a formation evaluation (e.g., per theevaluation block 314), and/or other data, e.g., as accessed from one ormore databases (e.g., maintained by one or more servers, etc.). As anexample, a well trajectory may take into consideration various “basis ofdesign” (BOD) constraints, such as general surface location, target(e.g., reservoir) location, and the like. As an example, a trajectorymay incorporate information about tools, bottom-hole assemblies, casingsizes, etc., that may be used in drilling the well. A well trajectorydetermination may take into consideration a variety of other parameters,including risk tolerances, fluid weights and/or plans, bottom-holepressures, drilling time, etc.

As an example, a workflow may progress to a first engineering serviceprovider (e.g., one or more processing machines associated therewith),which may validate a well trajectory and, for example, relief welldesign (see, e.g., the validation block 328). Such a validation processmay include evaluating physical properties, calculations, risktolerances, integration with other aspects of a workflow, etc. As anexample, one or more parameters for such determinations may bemaintained by a server and/or by the first engineering service provider;noting that one or more model(s), well trajectory(ies), etc. may bemaintained by a server and accessed by the first engineering serviceprovider. For example, the first engineering service provider mayinclude one or more computing systems executing one or more softwarepackages. As an example, where the first engineering service providerrejects or otherwise suggests an adjustment to a well trajectory, thewell trajectory may be adjusted or a message or other notification sentto the G&G service provider requesting such modification.

As an example, one or more engineering service providers (e.g., first,second, etc.) may provide a casing design, bottom-hole assembly (BHA)design, fluid design, and/or the like, to implement a well trajectory(see, e.g., the design block 338). In some embodiments, a secondengineering service provider may perform such design using one of moresoftware applications. Such designs may be stored in one or moredatabases maintained by one or more servers, which may, for example,employ STUDIO® framework tools, and may be accessed by one or more ofthe other service providers in a workflow.

As an example, a second engineering service provider may seek approvalfrom a third engineering service provider for one or more designsestablished along with a well trajectory. In such an example, the thirdengineering service provider may consider various factors as to whetherthe well engineering plan is acceptable, such as economic variables(e.g., oil production forecasts, costs per barrel, risk, drill time,etc.), and may request authorization for expenditure, such as from theoperating company's representative, well-owner's representative, or thelike (see, e.g., the formulation block 334). As an example, at leastsome of the data upon which such determinations are based may be storedin one or more database maintained by one or more servers. As anexample, a first, a second, and/or a third engineering service providermay be provided by a single team of engineers or even a single engineer,and thus may or may not be separate entities.

As an example, where economics may be unacceptable or subject toauthorization being withheld, an engineering service provider maysuggest changes to casing, a bottom-hole assembly, and/or fluid design,or otherwise notify and/or return control to a different engineeringservice provider, so that adjustments may be made to casing, abottom-hole assembly, and/or fluid design. Where modifying one or moreof such designs is impracticable within well constraints, trajectory,etc., the engineering service provider may suggest an adjustment to thewell trajectory and/or a workflow may return to or otherwise notify aninitial engineering service provider and/or a G&G service provider suchthat either or both may modify the well trajectory.

As an example, a workflow can include considering a well trajectory,including an accepted well engineering plan, and a formation evaluation.Such a workflow may then pass control to a drilling service provider,which may implement the well engineering plan, establishing safe andefficient drilling, maintaining well integrity, and reporting progressas well as operating parameters (see, e.g., the blocks 344 and 348). Asan example, operating parameters, formation encountered, data collectedwhile drilling (e.g., using logging-while-drilling ormeasuring-while-drilling technology), may be returned to a geologicalservice provider for evaluation. As an example, the geological serviceprovider may then re-evaluate the well trajectory, or one or more otheraspects of the well engineering plan, and may, in some cases, andpotentially within predetermined constraints, adjust the wellengineering plan according to the real-life drilling parameters (e.g.,based on acquired data in the field, etc.).

Whether the well is entirely drilled, or a section thereof is completed,depending on the specific embodiment, a workflow may proceed to a postreview (see, e.g., the evaluation block 318). As an example, a postreview may include reviewing drilling performance. As an example, a postreview may further include reporting the drilling performance (e.g., toone or more relevant engineering, geological, or G&G service providers).

Various activities of a workflow may be performed consecutively and/ormay be performed out of order (e.g., based partially on information fromtemplates, nearby wells, etc. to fill in any gaps in information that isto be provided by another service provider). As an example, undertakingone activity may affect the results or basis for another activity, andthus may, either manually or automatically, call for a variation in oneor more workflow activities, work products, etc. As an example, a servermay allow for storing information on a central database accessible tovarious service providers where variations may be sought bycommunication with an appropriate service provider, may be madeautomatically, or may otherwise appear as suggestions to the relevantservice provider. Such an approach may be considered to be a holisticapproach to a well workflow, in comparison to a sequential, piecemealapproach.

As an example, various actions of a workflow may be repeated multipletimes during drilling of a wellbore. For example, in one or moreautomated systems, feedback from a drilling service provider may beprovided at or near real-time, and the data acquired during drilling maybe fed to one or more other service providers, which may adjust itspiece of the workflow accordingly. As there may be dependencies in otherareas of the workflow, such adjustments may permeate through theworkflow, e.g., in an automated fashion. In some embodiments, a cyclicprocess may additionally or instead proceed after a certain drillinggoal is reached, such as the completion of a section of the wellbore,and/or after the drilling of the entire wellbore, or on a per-day, week,month, etc. basis.

Well planning can include determining a path of a well that can extendto a reservoir, for example, to economically produce fluids such ashydrocarbons therefrom. Well planning can include selecting a drillingand/or completion assembly which may be used to implement a well plan.As an example, various constraints can be imposed as part of wellplanning that can impact design of a well. As an example, suchconstraints may be imposed based at least in part on information as toknown geology of a subterranean domain, presence of one or more otherwells (e.g., actual and/or planned, etc.) in an area (e.g., considercollision avoidance), etc. As an example, one or more constraints may beimposed based at least in part on characteristics of one or more tools,components, etc. As an example, one or more constraints may be based atleast in part on factors associated with drilling time and/or risktolerance.

As an example, a system can allow for a reduction in waste, for example,as may be defined according to LEAN. In the context of LEAN, considerone or more of the following types of waste: Transport (e.g., movingitems unnecessarily, whether physical or data); Inventory (e.g.,components, whether physical or informational, as work in process, andfinished product not being processed); Motion (e.g., people or equipmentmoving or walking unnecessarily to perform desired processing); Waiting(e.g., waiting for information, interruptions of production during shiftchange, etc.); Overproduction (e.g., production of material,information, equipment, etc. ahead of demand); Over Processing (e.g.,resulting from poor tool or product design creating activity); andDefects (e.g., effort involved in inspecting for and fixing defectswhether in a plan, data, equipment, etc.). As an example, a system thatallows for actions (e.g., methods, workflows, etc.) to be performed in acollaborative manner can help to reduce one or more types of waste.

As an example, a system can be utilized to implement a method forfacilitating distributed well engineering, planning, and/or drillingsystem design across multiple computation devices where collaborationcan occur among various different users (e.g., some being local, somebeing remote, some being mobile, etc.). In such a system, the varioususers via appropriate devices may be operatively coupled via one or morenetworks (e.g., local and/or wide area networks, public and/or privatenetworks, land-based, marine-based and/or areal networks, etc.).

As an example, a system may allow well engineering, planning, and/ordrilling system design to take place via a subsystems approach where awellsite system is composed of various subsystem, which can includeequipment subsystems and/or operational subsystems (e.g., controlsubsystems, etc.). As an example, computations may be performed usingvarious computational platforms/devices that are operatively coupled viacommunication links (e.g., network links, etc.). As an example, one ormore links may be operatively coupled to a common database (e.g., aserver site, etc.). As an example, a particular server or servers maymanage receipt of notifications from one or more devices and/or issuanceof notifications to one or more devices. As an example, a system may beimplemented for a project where the system can output a well plan, forexample, as a digital well plan, a paper well plan, a digital and paperwell plan, etc. Such a well plan can be a complete well engineering planor design for the particular project.

FIG. 8 shows a schematic diagram depicting an example of a drillingoperation of a directional well in multiple sections. The drillingoperation depicted in FIG. 8 includes a wellsite drilling system 800 anda field management tool 820 for managing various operations associatedwith drilling a bore hole 850 of a directional well 817. The wellsitedrilling system 800 includes various components (e.g., drillstring 812,annulus 813, bottom hole assembly (BHA) 814, kelly 815, mud pit 816,etc.). As shown in the example of FIG. 8, a target reservoir may belocated away from (as opposed to directly under) the surface location ofthe well 817. In such an example, special tools or techniques may beused to ensure that the path along the bore hole 850 reaches theparticular location of the target reservoir.

As an example, the BHA 814 may include sensors 808, a rotary steerablesystem 809, and a bit 810 to direct the drilling toward the targetguided by a pre-determined survey program for measuring location detailsin the well. Furthermore, the subterranean formation through which thedirectional well 817 is drilled may include multiple layers (not shown)with varying compositions, geophysical characteristics, and geologicalconditions. Both the drilling planning during the well design stage andthe actual drilling according to the drilling plan in the drilling stagemay be performed in multiple sections (e.g., sections 801, 802, 803 and804) corresponding to the multiple layers in the subterranean formation.For example, certain sections (e.g., sections 801 and 802) may usecement 807 reinforced casing 806 due to the particular formationcompositions, geophysical characteristics, and geological conditions.

In the example of FIG. 8, a surface unit 811 may be operatively linkedto the wellsite drilling system 800 and the field management tool 820via communication links 818. The surface unit 811 may be configured withfunctionalities to control and monitor the drilling activities bysections in real-time via the communication links 818. The fieldmanagement tool 820 may be configured with functionalities to storeoilfield data (e.g., historical data, actual data, surface data,subsurface data, equipment data, geological data, geophysical data,target data, anti-target data, etc.) and determine relevant factors forconfiguring a drilling model and generating a drilling plan. Theoilfield data, the drilling model, and the drilling plan may betransmitted via the communication link 818 according to a drillingoperation workflow. The communication links 818 may include acommunication subassembly.

During various operations at a wellsite, data can be acquired foranalysis and/or monitoring of one or more operations. Such data mayinclude, for example, subterranean formation, equipment, historicaland/or other data. Static data can relate to, for example, formationstructure and geological stratigraphy that define the geologicalstructures of the subterranean formation. Static data may also includedata about a bore, such as inside diameters, outside diameters, anddepths. Dynamic data can relate to, for example, fluids flowing throughthe geologic structures of the subterranean formation over time. Thedynamic data may include, for example, pressures, fluid compositions(e.g. gas oil ratio, water cut, and/or other fluid compositionalinformation), and states of various equipment, and other information.

The static and dynamic data collected via a bore, a formation,equipment, etc. may be used to create and/or update a three dimensionalmodel of one or more subsurface formations. As an example, static anddynamic data from one or more other bores, fields, etc. may be used tocreate and/or update a three dimensional model. As an example, hardwaresensors, core sampling, and well logging techniques may be used tocollect data. As an example, static measurements may be gathered usingdownhole measurements, such as core sampling and well loggingtechniques. Well logging involves deployment of a downhole tool into thewellbore to collect various downhole measurements, such as density,resistivity, etc., at various depths. Such well logging may be performedusing, for example, a drilling tool and/or a wireline tool, or sensorslocated on downhole production equipment. Once a well is formed andcompleted, depending on the purpose of the well (e.g., injection and/orproduction), fluid may flow to the surface (e.g., and/or from thesurface) using tubing and other completion equipment. As fluid passes,various dynamic measurements, such as fluid flow rates, pressure, andcomposition may be monitored. These parameters may be used to determinevarious characteristics of a subterranean formation, downhole equipment,downhole operations, etc.

To facilitate the processing and analysis of data, simulators may beused to process data. Data fed into the simulator(s) may be historicaldata, real time data or combinations thereof. Simulation through one ormore of the simulators may be repeated or adjusted based on the datareceived. As an example, oilfield operations can be provided withwellsite and non-wellsite simulators. The wellsite simulators mayinclude a reservoir simulator, a wellbore simulator, and a surfacenetwork simulator. The reservoir simulator may solve for hydrocarbonflowrate through the reservoir and into the wellbores. The wellboresimulator and surface network simulator may solve for hydrocarbonflowrate through the wellbore and the surface gathering network ofpipelines.

FIG. 9 shows an example of a system 900 that includes various componentsthat can be local to a wellsite and includes various components that canbe remote from a wellsite. As shown, the system 900 includes anorchestration block 902, an operation block 904, a core and servicesblock 906 and an equipment block 908. These blocks may be labeled in oneor more manners other than as shown in the example of FIG. 9. In theexample of FIG. 9, the blocks 902, 904, 906 and 908 can be defined byone or more of operational features, functions, relationships in anarchitecture, etc.

As an example, the blocks 902, 904, 906 and 908 may be described in apyramidal architecture where, from peak to base, a pyramid includes theblock 902, the block 904, the core and services block 906 and theequipment block 908.

As an example, the block 902 can be associated with a well managementlevel (e.g., well planning and/or orchestration) and can be associatedwith a rig management level (e.g., rig dynamic planning and/ororchestration). As an example, the block 904 can be associated with aprocess management level (e.g., rig integrated execution as fieldoperations). As an example, the core and services block 906 can beassociated with a data management level (e.g., sensor, instrumentation,inventory, etc.). As an example, the equipment block 908 can beassociated with a wellsite equipment level (e.g., wellsite subsystems,etc.).

As an example, the block 902 may receiving information from a drillingworkflow framework and/or one or more other sources, which may be remotefrom a wellsite.

In the example of FIG. 9, the block 902 includes a plan/replan block922, an orchestrate/arbitrate block 924 and a local resource managementblock 926. In the example of FIG. 9, the block 904 includes anintegrated execution block 944, which can include or be operativelycoupled to blocks for various subsystems of a wellsite such as adrilling subsystem, a mud management subsystem (e.g., a hydraulicssubsystem), a casing subsystem (e.g., casings and/or completionssubsystem), and, for example, one or more other subsystems. In theexample of FIG. 9, the core and services block 906 includes a datamanagement and real-time services block 964 (e.g., real-time or nearreal-time services) and a rig and cloud security block 968 (see, e.g.,the system 500 of FIG. 5 as to provisioning and various type of securitymeasures, etc.). In the example of FIG. 9, the equipment block 908 isshown as being capable of providing various types of information to thecore and services block 906. For example, consider information from arig surface sensor, a LWD/MWD sensor, a mud logging sensor, a rigcontrol system, rig equipment, personnel, material, etc. In the example,of FIG. 9, a block 970 can provide for one or more of datavisualization, automatic alarms, automatic reporting, etc. As anexample, the block 970 may be operatively coupled to the core andservices block 906 and/or one or more other blocks.

As mentioned, a portion of the system 900 can be remote from a wellsite.For example, to one side of a dashed line appear a remote operationcommand center block 992, a database block 993, a drilling workflowframework block 994, an enterprise resource planning (ERP) block 995 anda field services delivery block 996. Various blocks that may be remotecan be operatively coupled to one or more blocks that may be local to awellsite system. For example, a communication link 912 is illustrated inthe example of FIG. 9 that can operatively couple the blocks 906 and 992(e.g., as to monitoring, remote control, etc.), while anothercommunication link 914 is illustrated in the example of FIG. 9 that canoperatively couple the blocks 906 and 996 (e.g., as to equipmentdelivery, equipment services, etc.). Various other examples of possiblecommunication links are also illustrated in the example of FIG. 9.

As an example, the system 900 of FIG. 9 may be a field management tool.As an example, the system 900 of FIG. 9 may include a drilling framework(see, e.g., the drilling frameworks 304 and 620). As an example, blocksin the system 900 of FIG. 9 that may be remote from a wellsite mayinclude various features of the services 790 of FIG. 7.

As an example, a wellbore can be drilled according to a drilling planthat is established prior to drilling. Such a drilling plan, which maybe a well plan, can set forth equipment, pressures, trajectories and/orother parameters that define drilling process for a wellsite. As anexample, a drilling operation may then be performed according to thedrilling plan (e.g., well plan). As an example, as information isgathered, a drilling operation may deviate from a drilling plan.Additionally, as drilling or other operations are performed, subsurfaceconditions may change. Specifically, as new information is collected,sensors may transmit data to one or more surface units. As an example, asurface unit may automatically use such data to update a drilling plan(e.g., locally and/or remotely).

As an example, the drilling workflow framework 994 can be or include aG&G system and a well planning system. As an example, a G&G systemcorresponds to hardware, software, firmware, or a combination thereofthat provides support for geology and geophysics. In other words, ageologist who understands the reservoir may decide where to drill thewell using the G&G system that creates a three-dimensional model of thesubsurface formation and includes simulation tools. The G&G system maytransfer a well trajectory and other information selected by thegeologist to a well planning system. The well planning systemcorresponds to hardware, software, firmware, or a combination thereofthat produces a well plan. In other words, the well plan may be ahigh-level drilling program for the well. The well planning system mayalso be referred to as a well plan generator.

In the example of FIG. 9, various blocks can be components that maycorrespond to one or more software modules, hardware infrastructure,firmware, equipment, or any combination thereof. Communication betweenthe components may be local or remote, direct or indirect, viaapplication programming interfaces, and procedure calls, or through oneor more communication channels.

As an example, various blocks in the system 900 of FIG. 9 can correspondto levels of granularity in controlling operations of associated withequipment and/or personnel in an oilfield. As shown in FIG. 9, thesystem 900 can include the block 902 (e.g., for well plan execution),the block 904 (e.g., process manager collection), the core and servicesblock 906, and the equipment block 908.

The block 902 may be referred to as a well plan execution system. Forexample, a well plan execution system corresponds to hardware, software,firmware or a combination thereof that performs an overall coordinationof the well construction process, such as coordination of a drilling rigand the management of the rig and the rig equipment. A well planexecution system may be configured to obtain the general well plan fromwell planning system and transform the general well plan into a detailedwell plan. The detailed well plan may include a specification of theactivities involved in performing an action in the general well plan,the days and/or times to perform the activities, the individualresources performing the activities, and other information.

As an example, a well plan execution system may further includefunctionality to monitor an execution of a well plan to track progressand dynamically adjust the plan. Further, a well plan execution systemmay be configured to handle logistics and resources with respect to onand off the rig. As an example, a well plan execution system may includemultiple sub-components, such as a detailer that is configured to detailthe well planning system plan, a monitor that is configured to monitorthe execution of the plan, a plan manager that is configured to performdynamic plan management, and a logistics and resources manager tocontrol the logistics and resources of the well. In one or moreembodiments, a well plan execution system may be configured tocoordinate between the different processes managed by a process managercollection (see, e.g., the block 904). In other words, a well planexecution system can communicate and manage resource sharing betweenprocesses in a process manager collection while operating at, forexample, a higher level of granularity than process manager collection.

As to the block 904, as mentioned, it may be referred to as a processmanager collection. In one or more embodiments, a process managercollection can include functionality to perform individual processmanagement of individual domains of an oilfield, such as a rig. Forexample, when drilling a well, different activities may be performed.Each activity may be controlled by an individual process manager in theprocess manager collection. A process manager collection may includemultiple process managers, whereby each process manager controls adifferent activity (e.g., activity related to the rig). In other words,each process manager may have a set of tasks defined for the processmanager that is particular to the type of physics involved in theactivity. For example, drilling a well may use drilling mud, which isfluid pumped into well in order to extract drill cuttings from the well.A drilling mud process manager may exist in a process manager collectionthat manages the mixing of the drilling mud, the composition, testing ofthe drilling mud properties, determining whether the pressure isaccurate, and performing other such tasks. The drilling mud processmanager may be separate from a process manager that controls movement ofdrill pipe from a well. Thus, a process manager collection may partitionactivities into several different domains and manages each of thedomains individually. Amongst other possible process managers, a processmanager collection may include, for example, a drilling process manager,a mud preparation and management process manager, a casing runningprocess manager, a cementing process manager, a rig equipment processmanager, and other process managers. Further, a process managercollection may provide direct control or advice regarding the componentsabove. As an example, coordination between process managers in a processmanager collection may be performed by a well plan execution system.

As to the core and service block 906 (e.g., a core services block or CSblock), it can include functionality to manage individual pieces ofequipment and/or equipment subsystems. As an example, a CS block caninclude functionality to handle basic data structure of the oilfield,such as the rig, acquire metric data, produce reports, and managesresources of people and supplies. As an example, a CS block may includea data acquirer and aggregator, a rig state identifier, a real-time (RT)drill services (e.g., near real-time), a reporter, a cloud, and aninventory manager.

As an example, a data acquirer and aggregator can include functionalityto interface with individual equipment components and sensor and acquiredata. As an example, a data acquirer and aggregator may further includefunctionality to interface with sensors located at the oilfield.

As an example, a rig state identifier can includes functionality toobtain data from the data acquirer and aggregator and transform the datainto state information. As an example, state information may includehealth and operability of a rig as well as information about aparticular task being performed by equipment.

As an example, real-time (RT) drill services can include functionalityto transmit and present information to individuals and/or transmitinformation to one or more pieces of equipment (e.g., control signals,commands, etc.). As an example, the RT drill services can includefunctionality to transmit information to individuals involved accordingto roles and, for example, device types of each individual (e.g.,mobile, desktop, etc.). In one or more embodiments, informationpresented by RT drill services can be context specific, and may includea dynamic display of information so that a human user may view detailsabout items of interest.

As an example, in one or more embodiments, a reporter can includefunctionality to generate reports. For example, reporting may be basedon requests and/or automatic generation and may provide informationabout state of equipment and/or people.

As an example, a wellsite “cloud” framework can correspond to aninformation technology infrastructure locally at an oilfield, such as anindividual rig in the oilfield. In such an example, the wellsite “cloud”framework may be an “Internet of Things” (IoT) framework. As an example,a wellsite “cloud” framework can be an edge of the cloud (e.g., anetwork of networks) or of a private network.

As an example, an inventory manager can be a block that includesfunctionality to manage materials, such as a list and amount of eachresource on a rig.

In the example of FIG. 9, the equipment block 908 can correspond tovarious controllers, control unit, control equipment, etc. that may beoperatively coupled to and/or embedded into physical equipment at awellsite such as, for example, rig equipment. For example, the equipmentblock 908 may correspond to software and control systems for individualitems on the rig. As an example, the equipment block 908 may provide formonitoring sensors from multiple subsystems of a drilling rig andprovide control commands to multiple subsystem of the drilling rig, suchthat sensor data from multiple subsystems may be used to provide controlcommands to the different subsystems of the drilling rig and/or otherdevices, etc. For example, a system may collect temporally and depthaligned surface data and downhole data from a drilling rig and transmitthe collected data to data acquirers and aggregators in core services,which can store the collected data for access onsite at a drilling rigor offsite via a computing resource environment.

In one or more embodiments, a method can include performing dynamicscheduling of a plan, which can include rescheduling of a plan. In suchan example, a plan may be revised at least in part. As an example, aplan can be a well plan or, for example, a portion of a well plan. As anexample, various components at various levels of granularity may beconfigured to continually monitor performance of tasks at acorresponding level of granularity of a component and, for example,update the plan based on state information about the performance oftasks.

As used in the following discussion, components in different levels ofgranularity may each have an individual plan that is based on the levelof granularity. For example, a well plan execution system plan can be anoverall plan for a well or entire oilfield while a process managercollection process manages performance of domain plans that can bespecific to a respective process of a manager's domain. As an example, awell plan execution system may monitor and schedule tasks at a levelthat differs from that of an individual process manager level. Forexample, a well plan execution system may controls the execution ofactivities by process managers. As an example, a well plan executionsystem may enable interrelationships between process managers such that,for example, control information due to a delay of one process manageris transmitted to another process manager.

As an example, a plan can be a set of events or activities to be carriedout to change the state of a well or a component thereof from a firststate to a second state (e.g., a desired state) for the well orcomponent thereof. In such an example, a plan may define, for one ormore events: a list of any tasks in the plan that are to precede thetask, an action to which the task relates, and a condition for the task.The condition may be, for example, an authorizing precondition detailingcriterion that is to happen before the task may be performed, aconfirming condition defining when performance of the task is complete,and a failure condition defining when the performance of the task may bein error. For example, the failure condition may be the value of statesof oilfield equipment that is indicative of a failure to comply with theplan and a call for rescheduling.

Performing tasks according to the plan may include, based upon adetermination that one or more defined predecessor tasks for one or moretasks have been completed, and further starting at least one task of theplan, independently of time, based upon a determination that apre-authorizing condition has been met. Performance of a task may becontinually monitored to check for a failure condition being satisfied,and to check whether any confirming condition is satisfied. In someembodiments, the plan is scheduled according to time. In otherembodiments, management of the plan is time independent.

As an example, one or more obstacles may occur in implementation of aplan. Thus, for example, in one or more embodiments, a method maycontinuously reassess state(s) of a system; regenerate a plan thatregenerates a sequence of tasks in a second way (e.g., an optimal way).In one or more embodiments, regeneration can be performed continuallytaking into account a current state of an oilfield and a second state ofthe oilfield (e.g., desired state of the oilfield). In some embodiments,regeneration of a plan is performed when a failure condition isdetermined to exist.

In some embodiments, each portion of a system can be continuously and/orcontinually reassessed as to its state and a method can includegenerating a plan based on current state(s) to achieve a desired statefor one or more portions of the system. In other words, the processmanagers of process manager collection, when executing a plan, maycontinually obtain state information from equipment (e.g., one or moresubsystems through the core services) to identify one or more relevantstates of the system. If the state information indicates a delay orfailure condition, then the corresponding process managers of processmanager collection may re-plan to achieve the desired state. Forexample, the process manager may automatically regenerate the sequenceof tasks within the domain or level of granularity of the processmanager.

If the re-planning is not possible in a process manager's domain, thenre-planning may be elevated to a next level of granularity. For example,the re-planning from a particular process manager's domain may beelevated to the well plan execution system domain (e.g., passed from onelevel to another level).

As an example, a well planning system may have engineering expertise tomake design choices for an overall plan. In such a scenario, a well planexecution system may regenerate a plan optionally without involving thewell planning system, for example, as long as the new plan does notsubstantially alter engineering of the well. In particular, a well planexecution system might track resources that are being used by each of aplurality of process managers, but might not, for example, track one ormore individual tasks of each of the plurality of process managers.Thus, when a process manager is re-planning, a well plan executionsystem might track which resources are available before, during, and/orafter re-planning without having data regarding the details of the plan.In some embodiments the same re-planning may be used for multipleprocess managers and, in in some cases, a well plan execution system. Inother embodiments, at least some components of the system may use adifferent re-planning engine.

In one or more embodiments, dependency information is maintained atvarious levels of granularity and managed at the various levels ofgranularity. Thus, if a component performs planning (e.g., re-planning,etc.) that cause a delay in a dependent task, the component mayinstitute a change in the dependent task. If the change is with respectto a different domain, then the component may notify the process managerdirectly, or notify well plan execution system of the change.

As an example, a method can be a real-time well construction processthat includes inference through probabilistic data fusion. Such a methodcan be implemented as part of a control system, which may be utilized inone or more well construction operations. As an example, such a methodmay be implemented using one or more of the systems of FIGS. 2, 3, 4, 5,6, 7, 8 and 9.

As an example, a well construction process automation system can includeone or more interfaces that can receive information for tracking one ormore of equipment, a wellbore, and a process with a relatively highdegree of confidence, which can help to assure for safe and efficientoperations.

A system can include circuitry that performs robust state detection.Such circuitry can include a processor and executable-instructions thatprocess information. Such circuitry can operate using one or moremodels, which may have associated degrees of uncertainty. Such circuitrycan operate using information from one or more sensors where suchinformation may include noise, artifacts, etc.

As an example, a control system can utilize information to determine oneor more types of bit-rock interactions. For example, such a system canacquire various types of information and process that information todetermine status of a bit with respect to rock. Such a status may be acontact status where a bit is interacting with rock via contact betweenthe bit and the rock. As a bit may be tens of meters or hundreds ofmeters or more in a borehole, bit-rock interactions can be uncertain asdefinitive sensor measurements may be unavailable. Further, transmissionof information acquired by one or more sensors of a drillstring may beaffected by various conditions (e.g., movement, fluid flow, pressures,vibrations, quality of transmission medium or media, etc.). As anexample, a system can include an interface for acquiring information andcircuitry for processing such information in a probabilistic manner toinfer a bit-rock interaction, which may be inferred as occurring inreal-time.

As an example, a system can receive data and analyze data for wellconstruction state inference, for example, to infer one or more types ofbit-rock interaction.

As an example, a system can utilize one or more probabilistic mixturemodels. A probabilistic mixture model (PMM) can be a trained model(e.g., or trainable model) that is adapted based on input sensor data,for example, for series data, which can include time and/or depth seriesdata. Examples of data can be input sensor data stream from a surfacetorque sensor and from a hook load sensor. A trained PMM can be adaptedin a manner that accounts for noise and suitable priors.

A trained PMM can be utilized in an online system for classification ofobservations related to an underlying state of operations (e.g. onbottom drilling bit-rock interaction versus off bottom rotating where abit is not interacting with rock to further a borehole). As an example,a system can provide for multiple classified observables from differenttypes of measurements that can be subsequently fused into states, forexample, using a Bayesian network, giving a robust state detection underuncertainty. In such an example, the states can correspond tooperational states, which may include different types of operationalstates for a bit with respect to rock.

In various examples, a system can utilize trained models trained viadrilling mechanics knowledge, for example, in the interpretation ofclassifications (e.g., via PMM approach) as well as in inferencegeneration (e.g., via a Bayesian network model approach).

In various trials, an example system was implemented for a rigsite wherethe system received real sensor data (hook load and surface torque) forthe inference of specified states of bit-rock interaction, includingstates such as, for example: (a) no interaction, (b) bit fully engagedwith formation, and (c) in transition.

In the trials, streaming input data were utilized to continuously learna probabilistic mixture model (PMM) in a manner that allowed fortracking of levels. For example, hook load can be modeled as a mixtureof several distributions that evolve as well construction progresses(e.g., stages of a well, etc.) correlated to a drill string being inslips, out of slips off bottom, or on bottom rotating. Observationsinferred from mixture models can include various levels of confidence. Asystem's implementation of a Bayesian network can allow for fusion ofinformation into a robust system state (e.g., bit-rock interaction,etc.).

As an example, a system can utilize a Bayesian network backed by amixture model to provide fast, adaptive and robust detection of one ormore states from drilling operation time series data with complextemporally correlated patterns. By learning from data and using priorsfrom domain experts, inference features of such a system can optionallybe operated without user tuned thresholds or parameters. A system, asbeing or including a computational framework with appropriateinterfaces, can include features for implementing a probabilisticBayesian approach to characterize and act on uncertainty in drillingsystems (e.g., rigsite systems, etc.). Such a system can be extensiblein that, for example, additional observations drawn from new types ofmeasurements can be integrated to detect additional states and/or reduceuncertainty in a core set of states.

As an example, a system can operate to extract elementary features fromdata channels (e.g., raw, filtered, etc.) where the system includescategorical variables with probabilities for each possible value. Such asystem can include defined feature types such as, for example, levelsand trends. In such an example, the levels can include level states andthe trends can include trend states. As an example, level states may bediscerned from data using a probabilistic mixture model (PMM) such as,for example, a Gaussian mixture model (GMM). As an example, trend statesmay be discerned using change detection (e.g., a change detectionengine, etc.). As an example, a system can provide for fusion ofextracted features to infer one or more “hidden” states, which caninclude one or more states that are not readily observable. For example,consider a state of a bit that is defined with respect to material suchas rock of a formation that is being drilled into as part of aconstruction process that forms a well where the well can be utilizedfor injection and/or production of fluid (e.g., liquid and/or gas).

As an example, a system can utilize levels modeled via a GMM, trendsdetected via a change detection engine, a domain modeled as a Bayesiannetwork (e.g., a Bayesian Belief Network or BNN) that can provide forfusion of information as evidence where feature probabilities are usedthrough a “virtual” evidence paradigm (see, e.g., FIG. 25). As anexample, so-called “hard” evidence can for levels (e.g., as analyzed viaone or more GMMs) and, for example, so-called “virtual” evidence can bethrough trends as probabilities such as, for example, a probability ofbeing on-bottom as determined from drillstring movement (e.g., and/orbeing off bottom) and a probability of a trend as to a block position(e.g., decreasing, stationary, or increasing). As an example, one ormore probability tables may be utilized where information therein may bebased at least in part on domain expertise (e.g., expert knowledge,etc.) and/or via refinement in a BBN model and/or via statisticallearning of probability tables.

In a BNN, beliefs can be defined as the probability that a variable willbe in a certain state based on an addition of evidence in a currentsituation. A special case of beliefs are the a priori beliefs that arebased solely on prior information. A priori beliefs can be stored indata structures in a computational system as one or more conditionalprobability tables (e.g., or other format). Evidence can be defined asbeing information about a current situation. As an example, a BNN methodcan consider as evidence a definite finding that a node X has aparticular value x (e.g., X=x). This kind of evidence often can bereferred to as specific evidence or hard evidence. For example, in thecontext of medicine, suppose that the patient has flu, then it would beFlu=True, which is specific evidence. On the other hand, the evidencemight be simply a probability distribution over the node X. For example,suppose a doctor is not completely sure whether the patient has anallergy or not. The doctor, through expert knowledge in the doctor'sdomain, thinks that the patient has an allergy but the doctor isapproximately 70 percent sure as to that thought. Such evidence in a BNNcan be referred to as virtual evidence (e.g., or likelihood evidence orsoft evidence). Virtual evidence may be defined as a generalization ofstandard evidence (e.g., hard evidence) in a BNN. Virtual evidence maybe specified when a specific state of a discrete variable is unknown,yet information exists about chances of the discrete variable being inparticular states.

Virtual evidence can be defined to be a probability of evidence. Virtualevidence can be utilized for incorporating uncertainty of evidence intoa BNN. For example, virtual evidence can be included by adding a virtualevidence node as a child of a regular evidence node in a network (see,e.g., child node in FIG. 25 and associated conditional probabilities).In such an example, evidence can be set as virtual evidence on a virtualevidence (VE) child node rather than on its parent node directly. Thisvirtual evidence may be set via a conditional probability table for theVE child node. As the VE child node is a descendant of its parent node,Bayesian inference can be utilized to update the probability of theparent node. For example, consider a weather scenario where a nodeCloudy includes a child VE Cloudy. In such an example, virtual evidencecan be set as Cloudy=0.75 and not Cloudy=0.25, then a probability can becalculated P(Cloudy|VE Cloudy) as follows (P(VE Cloudy|Cloudy)P(Cloudy)/P(VE Cloudy). The parent node Cloudy may be in a graph withanother node, Rain where Cloudy can influence Rain.

As an example, a computational framework can include one or more datastructures that include one or more probabilities (e.g., probabilitytables, etc.) that store values that may be based on expert knowledge(e.g., one or more drillers, etc.). While expert knowledge is mentioned,such a system may include one or more data structures with such one ormore probabilities that are estimates, which may be based on informationof a database from offset wells, information from a collection ofoperational personal, information based on one or more physical models,etc. As an example, virtual evidence may be provided as to one or morepieces of equipment such as one or more sensors where output may beuncertain, noisy, etc. For example, uncertainty of a value output by asensor can be characterized via one or more conditional probabilities,which may be associated with a child node as a VE node that has acorresponding parent node. Such uncertainty may be determined viaoperation of the sensor in controlled conditions and in situ during oneor more field operations and/or in controlled conditions that mimic insitu conditions. An analysis of performance may demonstrate that aprobability can be assigned to a value for one or more in situconditions that can occur during field operations. Such a probabilitymay be specified in a data structure as associated with a virtualevidence (VE) node that has an associated parent node.

As an example, virtual evidence can be utilized to handle observationuncertainty drawn for instance from noisy sensors or indirectmeasurements. As an example, rig sensor data can be utilized to computevirtual evidence (e.g., as one or more child nodes, etc.) for variablessuch as, for example, on-bottom from drillstring movement and blockposition trend.

FIG. 10 shows an example of a method 1000 that includes an acquisitionblock 1010 for acquiring data during rig operations that includeoperations that utilize a bit to drill rock where the data includedifferent types of data; an analysis block 1020 for analyzing the datautilizing a probabilistic mixture model for modes, a detection enginefor trends and a Bayesian network model for an inference; and an outputblock 1030 for outputting information as to the inference where theinference characterizes a relationship between the bit and the rock. Themethod 1000 can also include a control block 1040 for controlling atleast one of the rig operations using the information. As mentioned, asystem can include one or more interfaces that can acquire one or moretypes of data, which can include series data such as time and/or depthseries data.

The method 1000 is shown in FIG. 10 in association with variouscomputer-readable medium (CRM) blocks 1011, 1021, 1031, and 1041. Suchblocks generally include instructions suitable for execution by one ormore processors (or cores) to instruct a computing device or system toperform one or more actions. While various blocks are shown, a singlemedium may be configured with instructions to allow for, at least inpart, performance of various actions of the method 1000. As an example,a CRM block can be a computer-readable storage medium that isnon-transitory, not a carrier wave and not a signal. As an example, suchblocks can include instructions that can be stored in memory such as thememory 794 of the system 790 and can be executable by one or more of theprocessors 793 of the system 790. As an example, the method 1000 may beperformed in a system such as one or more of the systems of FIG. 2, FIG.3, FIG. 4, FIG. 5, FIG. 6, FIG. 7, FIG. 8, FIG. 9, FIG. 11, FIG. 15,FIG. 26, FIG. 29 or FIG. 30.

FIG. 11 shows an example of a rig system 1100 that includes a rig thatcan be utilized to perform operations that utilize a bit to drill rock.In the example of FIG. 11, a substantially vertical system isillustrated with a hole depth and a bit depth. Where the bit depth isequal to the hole depth, the bit can be in contact with the bottom ofthe hole (BOH). However, contact can occur with or without weight (e.g.,weight on bit or WOB) and can occur with or without rotation of the bit(e.g., bit rotation or BR). As an example, an operation can includemoving a bit closer to the bottom of the hole and an operation caninclude moving a bit away from the bottom of the hole.

In the example of FIG. 11, the rig system 1100 includes various sensorsthat can receive signals and covert the signals to digital data, whichcan be transmitted, for example, as a data stream. In such an example, adata stream can be a stream of real-time data. For example, as theweight on bit (WOB) changes during a drilling operation, the data streamcan be a time series of data that includes values that vary over timecorrespondingly as the WOB varies.

As an example, consider a measurement of weight made with a hydraulicgauge attached to a dead line of a drilling line. As the tensionincreases in the drilling line, more hydraulic fluid is forced throughthe gauge, turning the hands of the indicator, causing a digitalresponse, etc. The weight that is measured tends to includessubstantially everything exerting tension on the drilling line,including the traveling block(s) and the drilling line itself. Hence, tohave an accurate weight measurement of a drillstring, the driller canmake a zero offset adjustment to account for the traveling block(s) anditems other than the drillstring. With adjustments, the indicated weightwill represent the drillstring (e.g., drillpipe and bottom hole assembly(BHA)).

During drilling operations, a driller may be interested in the measuredweight for one or more operations. As mentioned, the weight of interestcan be the weight applied to the bit on the bottom of the hole (WOB). Asan example, a driller can take the rotating and hanging off bottomweight, say 136,200 kg, and subtract from that the amount of rotating onbottom weight, say 113,500 kg, to get a bit weight of 22,700 kg. Variousrigs can include a weight indicator that has a second indicator dialthat can be set to read zero (“zeroed”) with the drillstring hangingfree, where the second indicator dial works backwards from the mainindicator dial. After proper zeroing, a weight set on bottom (that takesweight away from the main dial), has the effect of adding weight to thissecondary dial, so that the driller can read weight on bit directly fromthe dial.

As may be appreciated, weight on bottom (WOB) can be approximate.Factors such as friction, fluid, debris, buoyancy, etc. can have effectson WOB measurements (e.g., as scalar values), stability of WOBmeasurements, etc. Hysteresis can exist such that WOB measurementsdiffer depending on a direction of a drillstring moving in a hole. Forexample, moving in a direction of gravity may result in different timeseries data than moving in a direction contrary to gravity.

During the drilling process, a driller can seek to identify the state ofa bit engaging with the bottom of the hole, which can involve use ofmore than one type of measurement, which can be via different types ofsensors of a rig system. As an example, a driller may think that a bitis on bottom, engaging with rock on the bottom of a whole. As anexample, one or more thoughts of a driller as to one or more operationalconditions, etc., may be specified quantitatively in the form of one ormore probabilities (e.g., as one or more data structures).

As an example, a surface hookload measurement can drop as soon as thebottom of the hole is engaged with the bit and the surface torquemeasurement can show an increased torque demand as the bit interactswith a formation (e.g., rock) and, if there is a downhole motor, surfacepressure can increase, signaling an increase in differential pressure asthe motor drills away. Such physical indicators can be present on therig floor with relatively adequate fidelity and provide a sense ofawareness for the driller that the equipment being operates is operatingto crush through rock and make steady progress drilling ahead. The way adriller infers an operational state as being one of on or off bottom isgenerally through experience and with some amount of uncertainty as oneor more transition states can exist between the two states of on and offbottom.

An approach to infer that the bit is on bottom can be by utilizing bitdepth and hole depth measurements. A system can infer that the bit is onbottom whenever the depth tracking system observes the bit depth andhole depth measurements to be equal within a small tolerance. Such anapproach can be sufficient for depth tracking, but it tends to lack theadditional breadth of indicators if an automated system (e.g., orsemi-automated system) is to identify drill on or drill off periods.Thus, to implement control (e.g., some degree of automation), a morecomplex state detection system can be implemented that accounts forstates in addition to the states of the bit being on or off bottom.

As an example, a method can provide for robust state detection using aGaussian mixture model (GMM) for clustering in combination with otherindicators fused within a Bayesian belief network (BBN), to identify astate of a bit as it transitions from being off bottom in a wellbore toa ramp up of engagement with a formation and sustaining an intendedweight on bit to drill ahead. The result of such a state detectionapproach can provide a binary answer of either being off or on bottomand can also infer transition states that can be leveraged for futurestate detection algorithms.

As mentioned with respect to the method 1000 of FIG. 10, a method caninclude analysis via a probabilistic mixture model and a Bayesiannetwork model. For example, a method can include making stateestimations based on the extraction of elementary observed pieces ofinformation from sensed data and the fusion of these observations as acombined answer for a “hidden” state in question. For example, a hiddenstate can be a bit-rock interaction state.

A particular example of a PMM is a Gaussian mixture model (GMM), whichcan be utilized for learning and classification of signal levels. Amethod can also utilize a change detection method for learning andclassification of signal trends. As an example, a method can includefusing of learned level states and trend states with additionalobservations for hidden state inference. In such an approach, levelstates can be states associated with physical conditions that areinfluenced by bit-rock interaction(s) while trend states can be statesassociated with physical conditions that directly influence bit-rockinteraction(s). In such an approach, the bit-rock interaction(s) are“sandwiched” between physical conditions that can cause bit-rockinteraction(s) and physical conditions that can be results of bit-rockinteraction(s). As an example, a block position can be input for a trendstate (e.g., with states decreasing, stationary and increasing) andhookload levels can be input for a level state (e.g., with states of inslips, off bottom and on bottom). In such an example, bit-rockinteractions can be represented as states including, for example,ramping up, fully engaged, ramping down and no interaction.

As mentioned, a PMM may be utilized to learn and classify states wheresuch a PMM can be a GMM. A Gaussian mixture model is a probabilisticmodel that assumes that observed data points are generated from amixture of a finite number of Gaussian distributions which can includeunknown parameters.

Below is an Example of a Univariate Model for a GMM:

$\begin{matrix}{{p(x)} = {\sum_{i = 1}^{k}{= {\phi_{i}{\mathcal{N}\left( {\left. x \middle| \mu_{i} \right.,\sigma_{i}} \right)}}}}} & (1) \\{{\mathcal{N}\left( {\left. x \middle| \mu_{i} \right.,\sigma_{i}} \right)} = {\frac{1}{\sigma_{i} \cdot \sqrt{2\pi}}{e\left( \frac{\left( {x - \mu_{i}} \right)^{2}}{2\sigma i^{2}} \right)}}} & (2) \\{{\sum_{i = 1}^{k}\phi_{i}} = 1} & (3)\end{matrix}$

FIG. 12 shows an example graphical user interface (GUI) 1200 thatincludes plots of hookload data (e.g., as a data stream, etc.) and ofdistributions that can be defined via distribution equations such as aGaussian distribution equations as in a GMM. In the example of FIG. 12,a method can include receiving the hookload data and generating thedistributions, which can be part of a GMM learning process that involveslearning multiple distributions where the multiple distributions areassociated with multiple states. In the example of FIG. 12, the statesare relatively distinct and give rise to Gaussian distributions withdifferent means and different standard deviations (e.g., parameters thatcan define a Gaussian distribution).

More specifically, FIG. 12 shows a univariate Gaussian mixture modelwith two clusters or modes (e.g., a multimodal distribution within thehookload data). The two clusters or modes are Gaussian kernels of data,as shown in the hookload data plot. One cluster or mode is for aGaussian distribution around 100 kkgf, while the other cluster or modeis for a Gaussian distribution around 20 kkgf (scaled up 10 times forbetter visualization). Thus, through GMM learning, two states can bediscerned from the hookload data, which may be a live data stream,previously acquired data, etc. As an example, a GMM learning process mayoccur in real-time based on real-time data streamed to a system. In suchan example, a GUI may be rendered to a display such as the GUI 1200 thatincludes a plot of the data and a plot of a distribution of the datawhere evidence of multiple modes may appear to indicate that evidence ofmultiple states exists in the data. In such an example, a user may viewthe modes as they evolve (e.g., come into existence, change, etc.) overtime as operations are occurring at a rig site (e.g., or rig sites).

Given a univariate model's parameters, the conditional probability thata data point x belongs to cluster C_(i) is:

$\begin{matrix}{{p\left( C_{i} \middle| x \right)} = {\frac{p\left( {x,C_{i}} \right)}{p(x)} = {\frac{{p\left( x \middle| C_{i} \right)}{p\left( C_{i} \right)}}{\sum_{j = 1}^{k}{{p\left( x \middle| C_{j} \right)}{p\left( C_{j} \right)}}} = \frac{\phi_{i}{\mathcal{N}\left( {\left. x \middle| \mu_{i} \right.,\sigma_{i}} \right)}}{\sum_{j = 1}^{k}{\phi_{j}{\mathcal{N}\left( {\left. x \middle| \mu_{j} \right.,\sigma_{j}} \right)}}}}}} & (4)\end{matrix}$

In the equations (1) to (4),

(x|μ_(i),σ_(i)) is the probability density function of a Gaussiandistribution with mean μ_(i) and standard deviation σ_(i), and

ϕ_(i) is the weight of cluster C_(i) in a GMM.

The use of Gaussian mixture model is an example of an approach thatprovides a computational framework for classifying a hidden state (orhidden states) of a system by modeling observation samples as drawn froma Gaussian, which can vary over time depending on hidden state(s). Forexample, during rotary drilling, torque (e.g., surface torque level) maybe modeled as no rotation, off bottom rotation and on bottom rotationwhere these are deemed to be a set of predefined hidden states. In suchan example, torque data (e.g., surface torque or STOR) can be analyzed(e.g., via a GMM) such that each of these states can be identified(e.g., analysis by “observing” the torque data stream).

FIG. 13 shows an example GUI 1300 of surface torque data versus timewhere a GMM approach has been applied for learning different states asbeing associated with a plurality of individual distributions, definedas individual Gaussian distributions. As shown in FIG. 13, statesinclude not rotating (no rotation or NR), on bottom drilling (On BD),and off bottom rotation (Off BR). The NR state can be an in slips state.The GUI 1300 shows transitions from one state to another state as wellas multiple occurrences of each of the states over the period ofoperational time. In particular, transitions are as follows from Off BRto NR, to Off BR, to On BD, to Off BR, to NR, to Off BR, to On BD, toOff BR, to NR, to Off BR, to On BD, to Off BR, to NR, to Off BR, to OnBD, to Off BR, to On BD. In such an example, a hole may become deeperwhere the On BD state is occurring. The time periods of On BD can beanalyzed for purposes of torque in relationship to formation beingdrilled into, which may provide information as to type of formationand/or one or more other conditions being experienced during On BD. Thedata in the GUI 1300 can be analyzed for non-productive time (NPT) whereoperations are not making a contribution to penetration such that rateof penetration (ROP) is decreased.

A Gaussian mixture model can work with a data stream incrementally forclassifying states from a learned model. As to handling learning of aGMM from a data stream incrementally, a method can include performingvarious actions, which may include making an assumption that theindividual means of individual different states evolve over time withgradual rather than abrupt changes. As mentioned, a mean can be aparameter of a Gaussian distribution and a GMM can include multipleindividual means for corresponding multiple individual distributions.

Example Pseudocode Sequential Learning and Prediction Using GMM:

 Initialize: Define an initial GMM by (i) Number of clusters: k,   (ii)Cluster  means: p and (iii) Cluster variances: σ (iv) Cluster weights:w. Empty   buffer for learning: B  Function: Predict  Parameters: Datapoint to classify x   Given an existing GMM (w, μ, σ)   For each ClusterC in GMM (w, μ, σ)    Use Equation (4) to compute probabilities ofmembership of data point to C   End For   Update B with new data point  If B is full    UpdateGMM(B)    Empty(B)   End If  Return computedprobabilities  Function: UpdateGMM  Parameters: Buffer of points toupdate GMM: B, GMM (w, μ, σ)  For each bin B   Update means: μ,variances: σ, cluster weights: w using Equations 1, 2, and 3  End For

In the foregoing example pseudocode, a computational framework canimplement a memory storage device with a data buffer, as represented byB. Such a buffer can be implemented for buffering of points to update aGMM. For example, an update can be to one or more parameters of a GMMthat correspond to one or more distributions. As an example, adistribution can be a prior distribution (e.g., a Gaussian with a mean,variance, etc.) or a newly evolving distribution.

As an example, a distribution may diminish in time such that thecorresponding state is no longer a part of a GMM. As an example, a noiseor other type of threshold criterion may be utilized for determiningwhen a distribution is to be “recognized”. For example, a distributionmay be deemed viable when a certain number of data points have beenprocessed as giving rise to the distribution. As an example, aforgetting factor may be applied, which may expedite deletion of an olddistribution that is unlikely to occur again, which may help to diminishconfusion between a relevant distribution and an old distribution thatis no longer relevant. As an example, where a distribution has not beenadded to for a period of time (e.g., days or week(s)), that distributionmay be diminished and/or deleted in one or more manners, which canreduce a number of states, which can simplify operation of a state-basedcomputational framework (e.g., a computational state machine), which maybe part of a control system.

As an example, a method can include learning and classifying states viachange detection method. For example, consider a computational frameworkthat utilizes a change detection engine on a plurality of single datachannels to learn an underlying model for measurements of interest, andto generate real-time probabilities for a signal of a single datachannel to increase, decrease or remain stationary. For example, asmentioned, block position can be a type of data that can trend such thatit can be defined as decreasing, stationary or increasing. As anexample, a method can be implemented for change detection optionallywithout utilization of a predefined window size(s), which can result ina full range of segment sizes being appropriately identified. In otherwords, a method can operate without a priori information as to what is awindow size.

FIG. 14 shows an example of a GUI 1400 that includes a plot of sensordata and a plot of probabilities as to some examples of trends, whichinclude stationary (e.g., probability of being stationary), increasing(e.g., probability of increasing) and decreasing (e.g., probability ofdecreasing). A change detection method can include acquiring data, whichcan be live streaming data during an ongoing rig operation. For example,the measurement in the GUI 1400 can be block position where the methoddetects probabilities of behavior of the block, which are associatedwith actions that can have a direct effect on bit-rock interaction(s).As shown in FIG. 14, the measurement has a high probability of beingstationary, which is followed by an increasing probability of increasing(e.g., with a decrease in probability of being stationary), followed bya trend toward a return to being stationary where the scalar value ofthe measurement is not the same as the prior period of time where thescalar value of the measurement provided a high probability of beingstationary. As shown in the example of FIG. 14, probabilities as totrends can be independent of absolute values of measurements and can,rather, be based on changes in the values of the measurements withrespect to time (e.g., consider a derivative, a slope, a differencebetween two scalars, a changing mean value, etc.).

As an example, a system can include learning various states using sensordata and fusing learned states, which can include processing of one ormore other observations (e.g., data from a rig operation, rigoperations, etc.). As an example, a fusion process can implement aBayesian belief network, which may be a single network or a network thatincludes sub-networks. A Bayesian belief network is an example of a typeof Bayesian network.

As an example, a Bayesian network can include weights where the weightsare associated with sensor data acquired from equipment such asequipment in a field that performs one or more field operations. Forexample, a Bayesian network can include weights that are applied todata-based numbers where the data are acquired from equipment at arigsite, which can include surface and downhole equipment.

In terms of an arc of a graph of a network (e.g., directed acyclic graph(DAG), etc.), an individual arc may have a weight or value associatedwith it, indicating a strength of interaction between nodes that the arcconnects. The nature of such a weight can be application dependent. Forexample, it may represent a cost associated with a particular action,the strength of a connection between two nodes or, in the case ofprobabilistic models, the probability that a particular event willoccur. As an example, a Bayesian belief network (e.g., a Bayesiannetwork) can be conducive to understanding a scenario or scenarios asthey can be constructed such that a parent(s) of a variable can be adirect cause (e.g., toward a state, of a state, etc.). Such an approachcan help to facilitate a process of determining weights for arcs thatconnect nodes of a network (e.g., assessment of conditionalprobabilities, etc.). As mentioned, an approach can, for example,include accounting for results, which may be conditions, behaviors,etc., that are a consequence of a state or states, which may beevidenced, in some instances, during a transition to a state or states.As an example, a Bayesian network can be formulated with factors thathave a direct effect on a state or states and with factors that areinfluenced by such a state or states (e.g., or temporal transition(s)thereto).

As an example, a Bayesian network can be implemented as part of acomputational framework that includes one or more interfaces (e.g., oneor more network interfaces, etc.) that can receive data acquired at oneor more sites such as one or more rigsites. As an example, acomputational framework can include one or more processors, memory,interfaces, etc. As mentioned, a computational framework can includereceiving data, which may include sensor data from one or more sensors.As an example, a computational framework can provide for sensor fusionutilizing at least in part a Bayesian network (e.g., or Bayesiannetworks).

Sensor fusion refers to a class of problems where data from varioussources can be integrated to arrive at an interpretation of a situation(e.g., a scenario). For example, data from various rigsite sensors,which may be for different sampling rates, different data formats,different units, etc., can be integrated to determine a status of one ormore rigsite operations, which may include one or more operations thatare associated with drilling. As an example, a sensor fusion approachmay include receiving data from a plurality of sensors where a state canbe discerned for a system by integrating at least a portion of thereceived data.

As an example, parameters of a Bayesian network may be tuned as one ormore conditional probability tables, which may be relative weights ofthe Bayesian network. In such an example, data can be used to tuneparameters where the parameters have physical meaning as they refer toinput indicators.

As an example, a computational framework may include one or moreanalysis engines. As an example, an analysis engine can include one ormore features of the APACHE STORM engine (Apache Software Foundation,Forest Hill, Md.). As an example, a method can include implementing atopology that includes a directed acyclic graph (DAG). For example, theAPACHE STORM application can include utilization of a topology thatincludes a DAG. A DAG can be a finite directed graph with no directedcycles that includes many vertices (e.g., nodes) and edges, with eachedge directed from one vertex to another, such that there is no way tostart at any vertex v and follow a consistently-directed sequence ofedges that eventually loops back to v again. As an example, a DAG can bea directed graph that includes a topological ordering, a sequence ofvertices such that individual edges are directed from earlier to laterin the sequence. As an example, a DAG may be used to model differentkinds of information. As another example, an analysis engine can includeone or more features of the NETICA framework (Norsys Software Corp.,Vancouver, Canada), which includes features that generate and usenetworks to perform various kinds of inference where, for example, givena scenario with limited knowledge, appropriate values or probabilitiesmay be determined for unknown variables. As yet another example, ananalysis engine can include one or more features of the TENSOR FLOW(Google, Mountain View, Calif.) framework, which includes a softwarelibrary for dataflow programming that provides for symbolic mathematics,which may be utilized for machine learning applications such asartificial neural networks (ANNs), etc.

FIG. 15 shows an example of a system 1500 that includes an arrangementof features with respect to bit-rock interaction, which can beassociated with states such as, for example, ramping up, fully engaged,ramping down and no interaction. Such an arrangement can be or be partof a Bayesian network that is defined to perform inference on bit-rockinteraction. Such inferences can be to one or more of the states, whichcan be or include exclusive states in that where bit-rock interaction isof one state, one or more other states can be excluded.

In the system 1500, random variables include: set 1510 defined ason-bottom from drillstring movement: is either on or off bottom, and iscomputed based on block position movements while the drillstring is notin slips; set 1520 defined as block position trend: represents the blockposition movement dynamic, either going downwards (decreasing), upwards(increasing) or stationary; set 1530 defined as hookload levels:represents the hookload magnitude related to particular states: inslips, off bottom, on bottom; set 1540 defined as surface torque levels:represents the surface torque magnitude related to particular states: norotation, off bottom rotating, on bottom rotating; and set 1550 definedas bit-rock interaction: represents the interaction between the drillbit and the rock at the bottom of the hole; can be: ramping up (i.e.,weight on bit increasing), ramping down (i.e., weight on bitdecreasing), fully engaged (weight on bit reached plateau value suitablefor drilling), or no interaction.

As an example, rig sensor data can be used to compute virtual evidence(e.g., for various nodes not shown in FIG. 15), for example, as to thefirst two sets of variables listed (1510 on-bottom from drillstringmovement and 1520 block position trend). On the other hand, the bit-rockinteraction variable set 1550 can be defined to be unobserved (e.g.,“hidden”) and hence demand inference computations to get a posterioriprobabilities of each possible state. As mentioned, a graph can includenodes (e.g., vertices) that can include parents and children. Asmentioned, a virtual evidence (VE) node can be a child and can includean associated conditional probability data structure.

As to the edges of the arrangement of the system 1500 (e.g., which canbe a portion of a larger network, etc.), and their directions, considerthat the on-bottom state from drillstring movement as well as the blockmovement have a direct impact on the bit-rock interaction state.Specifically, on-bottom will favor states where the bit interacts withthe formation (e.g., ramping up/down and fully engaged); whereas, offbottom will induce the no interaction state. Further, a block going down(lowering) is a favoring factor for either a ramping-up for interactionor fully engaged state; whereas, a block going up (raising orramping-down) indicates impending off bottom condition; noting that astationary block will also have a ramping-down effect. Finally, thebit-rock interaction state itself has an influence on the hookload andsurface torque magnitude, hence the opposite edge direction.Specifically, a fully engaged bit tends to favor on bottom levels(rotating or not); whereas, other bit-rock interaction states will favoroff bottom or in slips states measurement readings.

Examples of trial results are presented herein that utilize a drillingdata set that includes hookload, surface torque and block positionsampled at a rate of 1 second. The data set includes over 12000 datapoints.

FIG. 16 shows example GUIs 1610, 1630 and 1640 of data, which may beGUIs that are rendered to a display or displays in real-time duringongoing operations. In such an approach, a user may view the multiplechannels of data as operations are being performed. GUIs can be renderedvia execution of instructions stored in memory of a device or deviceswhere such instructions can be executable by one or more processors(e.g., CPU(s), GPU(s), core(s), etc.).

As to hookload and surface torque levels (see, e.g., variables 1530 and1540 of the system 1500 of FIG. 15), a method can include use of aGaussian mixture model for classifying states of observed hookload andtorque to detect the bit interaction with formation. Such a GMM may beonline in that it can process real-time data as may be streamed fromequipment at a rigsite (e.g., to a computational framework, locatedlocally or remotely or part locally and part remotely).

FIG. 17 shows GUIs 1710 and 1730 where indications are included as to onbottom drilling (e.g., On BD). The GUI 1710 can be compared to theinformation in the GUI 1200 of FIG. 12, which includes identification oftwo modes as corresponding to two different states that can be evidencedfrom hookload data. The GUI 1730 can be compared to the GUI 1300 of FIG.13, which includes the indications of “On BD” as well as “NR”.

Specifically, in FIG. 17, hookload data and surface torque data areclassified using a GMM approach with three states. As to hookload dataof the GUI 1710, the states include open circles that represent inslips, hatched circles without borders that represent off bottom anddotted circles without borders that represent on bottom. As to surfacetorque data of the GUI 1730, open circles represent no rotation, hatchedcircles without borders represent off bottom rotating, and dottedcircles without borders represent on bottom rotating. Accordingly, asystem can include acquiring data from multiple channels, analyzing thedata in real-time and assigning a state to the data in real-time. In aGUI rendered to a display, color may be utilized to allow a user toreadily view and determine what state or states exist for data from oneor more of multiple channels. For example, in the GUIs 1710 and 1730,colors such as purple, green and yellow may be utilized for the threestates of each channel.

As illustrated in the GUIs 1710 and 1730, use of a GMM approach toclassify states provides acceptable performance. As a GMM is initializedwith default parameters, it is possible to start using the GMM evenbefore any learning starts. As a signal evolves over time, parameterlearning in a GMM can adapt to the changes and appropriately classifydata within a data stream.

As to the types of data in FIG. 17, such data are known to evolverelatively smoothly over time due to underlying physics associated withthe actual operations. An abrupt change may be an indication of anoperational issue, which may include an issue such as an equipmentfailure. Sequential learning of a GMM can be used for trackingdistributions that evolve; however, where a data stream starts evolvingrandomly or with large offsets over time, that may be an indication ofone or more types of issues.

As to block position trend, probabilities of the block positionincreasing, decreasing or remaining stationary can be computed with achange detection engine.

FIG. 18 shows example GUIs 1810 and 1830 for block position (1810) andprobability (1830) with respect to time, per the data samples asacquired at a sampling rate of approximately 1 Hz (e.g., one sample persecond). The GUIs 1810 and 1830 demonstrate how trend probabilities canbe learned and determined from sensor data, for example, streaming blockposition sensor data. As an example, the information of the GUI 1830 ofFIG. 18 may be utilized in the system 1500, for example, at the variableset 1520 for block position trend.

As mentioned, the system 1500 may be a computational framework orinclude a computational framework that can provide for control of one ormore operations at a rigsite. As an example, one or more control signalsmay be issued responsive to one or more determinations of the system1500. Such determinations may originate at one or more of 1510, 1520,1530, 1540 and 1550; noting that the determinations at 1550 can berelevant to operations, particularly where one or more of the bit-rockinteraction states is “hidden” (e.g., not amenable to reliableobservation). As an example, a control signal may call for control of adrillstring responsive to a determined bit-rock interaction state, whichmay act to change a measurement of a channel or channels (e.g., blockposition, hookload, torque, etc.). In such an example, the system 1500can receive data where such data may allow a user to confirm that anappropriate control effect is realized or being reasonably realizedresponsive to issuance of the control signal. In such an approach, thesystem 1500 can provide for control and feedback as to the consequencesof issuance of one or more control signals.

FIG. 19 shows example GUIs 1910 and 1930, which are zoomed-in versionsof the GUIs 1810 and 1830 of FIG. 18. Such zooming functionality may beprovided such that a user can readily discern the operations beingperformed and/or assess the functioning of the system 1500 (e.g., or aportion thereof), which may be in real-time. As an example, a displaymay include a presentation of GUIs such as those of FIG. 18 and apresentation of GUIs such as those of FIG. 19 where the zoomed-inversions of FIG. 19 are for current time so a user can readily see whatis happening in real-time over a period of minutes (e.g., how one ormore operations may be “trending”, etc.).

As to on and off bottom from drillstring movement, the block positioncan be utilized to track the drillstring movement when not in slips. Aninitial on bottom instant may be provided externally (e.g., either by auser or inferred from the bit and hole depth measurements as explainedwith respect to FIG. 11, etc.). In such an example, a measured distanceto bottom can then be computed, and transformed into complementaryprobabilities of being on and off bottom with, for example, a softmaxfunction.

A softmax function can be a normalized exponential function, which canbe a generalization of the logistic function that “squashes” aK-dimensional vector z of arbitrary real values to a K-dimensionalvector GM of real values, where each entry may be in a defined rangesuch as in the range (0, 1) where the entries add up to unity.

FIG. 20 shows example GUIs 2010 and 2030 of measured distance to bottom(2010) and on and off bottom probabilities derived from measureddistance to bottom (2030).

As to bit-rock interaction, as explained with respect to the system 1500of FIG. 15, corresponding states can be inferred via a Bayesian networkthat can take various inputs, noting that the arrows in FIG. 15 areshown as indicating physical connections and not necessarily data flowconnections. The system 1500 can include nodes (e.g., vertices) as maybe present in a BNN.

Various observations can be injected into a Bayesian network that ispart of a computational framework for computing probabilities for eachof a plurality of bit-rock interaction states.

FIG. 21 shows an example GUI 2100 of probability versus time whereprobabilities are plotted for four bit-rock interaction states. Suchprobabilities can be determined using, for example, one or more GMMclassifiers (e.g., as applied to one or more of hookload channel data,torque channel data, etc.) and one or more change detectors (e.g., asapplied to one or more of block position channel data, measured distanceto bottom channel data/derived data, etc.). For example, informationassociated with sets 1510 and 1520 can be input via one or more changedetectors that output probabilities as to states (e.g., trend states)and information associated with sets 1530 and 1540 can be input via oneor more PMMs (e.g., GMMs) that output states (e.g., level states). Asmentioned, trend states may be states with conditions that directlyaffect bit-rock interaction and level states may be states withconditions that are affected by bit-rock interaction.

FIG. 22 shows a series of GUIs 2210, 2230, 2250 and 2270 for hookloadchannel data, surface torque channel data, block position channel dataand probability of being in one or more bit-rock interaction states.

The information in FIG. 22 corresponds to a zoomed-in section ofhookload, surface torque and block position channels, which may be coded(e.g., via color, etc.) by inferred bit-rock interaction state. Forexample, consider a color coding scheme with blue: no interaction, red:ramping up, green: fully engaged, purple: ramping down. In FIG. 22,various types of circles are utilized to indicate the different inferredbit-rock interaction states as being plotted as markers over the data.Some circles include borders while others include shading, hatching,etc., without borders. The markers can be utilized to compare across thedifferent types of data to understand how data are changing or not withrespect to time with respect to inferred bit-rock interaction state(s).

In FIG. 22, the GUI 2270 includes a legend that indicates fully engaged,no interaction, ramping down and ramping up as, for example, associatedwith the bit-rock interaction 1550 of the system 1500 of FIG. 15. TheGUIs 2210 (hookload), 2230 (surface torque) and 2250 (block position)can be associated with the hookload levels 1530, the surface torquelevels 1540 and the block position trend 1520 of the system 1500 of FIG.15, respectively.

The various trials demonstrate that a GMM can model complex signals withacceptable approximation to real states. Such an approach allows for aclustering of the signal data to infer various underlying states fromeach channel. Bayesian networks are shown to be suited to perform datafusion among disparate observations where, for example, predominantfactors and causality relationships can be captured in the model graph(e.g., in a DAG, etc.). The probabilistic nature of Bayesian networksoffers a more balanced, less categorical, answer for a posterioribeliefs. As explained, a system can utilize a “virtual” evidence conceptthat allows for observations to be continuous in the form ofprobabilities, which enables a smoother influence on the overallinferred beliefs of the Bayesian network.

As an example, a system can be supplemented with various features. Forexample, consider a noise filter applied to one or more channels tofacilitate clustering with a GMM. As another example, consider one ormore rules that may be implemented to discern more complicated hiddenstates, which might not be readily captured (e.g., at a level sufficientfor recognition). For instance, a hookload measurement may tend to dropwhen the drillstring is lowered in the well, even far from bottom, wherethere is a presence of friction (hole drag) that acts upon thedrillstring. Such a phenomenon may be addressed by one or more rules,which may include data filtering or ignoring data during certaintransitions such that behaviors that are not germane directly tobit-rock interactions can be diminished to more reliably generate GMMs(or detection engines) that operate to infer particular bit-rockinteraction states. As to a Bayesian network, a method can includetuning as to conditional probabilities right. As an example, tuning maybe facilitated from domain knowledge (e.g., manual and/or semi-automatedtuning based on knowledge from one or more offset wells, etc.). As anexample, a method can include learning one or more of such priorprobabilities from historical data and/or ongoing offset well data(e.g., for one or more ongoing operations at one or more otherrigsites). For example, in a given field, tuning for one well may beutilized for tuning for another well where the two wells are drilled atleast in part in a common formation (e.g., lithology, reservoir,caprock, etc.).

As an example, the system 1500 of FIG. 15 can be implemented to performa method for determining real-time state inferences of a wellconstruction process. As explained, various observations may beextracted from raw signals in order to generate evidence to be fed intoa Bayesian network, with the purpose of estimating a bit-rockinteraction state. As explained, a clustering method based on a GaussianMixture Model (GMM) can be implemented for a channel or channels, whereeach channel may have its own GMM.

FIGS. 23, 24 and 25 pertain to various examples of methods that includeBayesian belief networks (BBNs). The methods are described with respectto particular scenarios. Bayesian belief networks are suited forrepresenting a domain of interest and combining observations in aprobabilistic manner. As mentioned, they can be represented as one ormore directed acyclic graphs (DAGs), with nodes corresponding to randomvariables (observed or not) and edges indicating the influence of nodesto one another.

FIG. 23 shows an example of a method 2300 that includes a BBN, whichincludes no observations. In FIG. 23, the three random variables aredefined as (1) Rain, which represents the current weather, eitherraining or not; (2) Sprinkler, which represents the state of a gardensprinkler system being on or off; and (3) Grass, which represents thestate of the lawn in the garden, wet or not. In the model, edges aredefined to model the flow of influence, or causality, between the threevariables, where edges are defined as: (1) The edge from Rain toSprinkler implies that the smart sprinkler system has a capability toreceive weather information and turn itself on or off depending onforecasts; (2) The edge from Rain to Grass represents the obvious effectof the rain making the grass wet; (3) The edge from Sprinkler to Grassrepresents the similar obvious effect the sprinkler system has on thegrass getting wet. As shown in FIG. 23, for each node, a table isdefined with prior probabilities for each of the node states. If thenode is under the influence of one or several other nodes (i.e., an edgepointing towards the node in question), the probabilities areconditioned by the states of the influential node(s).

Based on available data, the state of the random variables (nodes) canbe observed or not. If observed, the variable value is set to thecorresponding state with a probability of 1. It is also possible toinfer the state of unobserved variables, for example, computing the aposteriori probability for each unobserved variable states. This can beachieved by applying Bayes rule across the network.

FIG. 24 shows an example of a method 2400 where both Grass (Wet) andSprinkler (On) variables are observed. Based on the domainrepresentation and the computed inference, the probabilities of whetherit is raining or not have substantially moved towards a strong belief itis not raining (99%).

Furthermore, the case where variables observations are not certain canbe handled by adding a node to represent the virtual evidence for thevariable in question.

FIG. 25 shows an example of a method 2500 where a node is added torepresent virtual evidence with a conditional probability table. Such anapproach allows for handling uncertainty of the observation of thesprinkler system state, like a neighbor who would observe the gardenfrom far through a window. This neighbor would have a belief of thesprinkler state based on its true on/off state, without being certain.In such an example, one can see the impact of the uncertainty on theSprinkler observation, as a slight reduction of the belief that it isnot raining (91%).

Virtual evidence can handle observation uncertainty drawn, for example,from one or more noisy sensors, one or more indirect measurements, etc.

As explained, a well construction process automation system can providefor tracking of the state or states of equipment and/or operations witha degree of confidence as to safe and efficient operations. Robust statedetection can be performed in a manner to handle uncertain models anddata from imperfect sensors. As explained, a system such as the system1500 of FIG. 15 can provide for practical implementation of wellconstruction state inference, for instance bit interacting with rock andslips status.

As an example, a probabilistic mixture model (PMM) can be learned from awindowed input sensor data stream. For example, consider one or more ofsurface torque and hook load, which may take into account noise andsuitable priors. Such models can be trained (e.g., via learning) andused for online classification of observations related to the underlyingstate of operations (e.g. on bottom drilling versus off bottomrotating). A system can include features for determining multipleclassified observables from different types of measurements where, forexample, such classified observables can be fused into states using atemporal Bayesian network that provide for robust state detection underuncertainty. Such a system can operate via drilling mechanics knowledgeas to interpretation of classification and configuration and/or tuningof a Bayesian network model.

As to types of sensor data, examples can include hookload, surfacetorque, stand-pipe pressure, block position, etc. Various examples canprovide for the inference of elementary states such as, for example,slips status (e.g., in/out of slips), and bit interaction with rock(e.g., no interaction, bit fully engaged with formation, in transition).

The use of input data to continuously learn a PMM can allow for thetracking of levels. As mentioned, hookload can be modeled as the mixtureof several distributions evolving as the well progresses via variouswell construction operations where hookload may be correlated to thedrill string being in slips, out of slips off bottom, or on bottomrotating.

In an example, clustering using hookload (HKLD) sensor data can involveusing a GMM with a three hour moving window and four clusters. In atrial example, a cluster was identified at approximately 50 klbf relatedto a hookload level when a drillstring is in slips, and additionalclusters with identified centers between approximately 150 klbf and 250klbf, interpreted as being on bottom drilling and off bottom.

Observations inferred from one or more mixture models can have variouslevels of confidence. A system that utilizes a Bayesian network canallow for their fusion into a robust system state. For example,different observations can be made when going on bottom, whether inrotary or slide drilling mode.

FIG. 26 shows an example of a system 2600 that includes a Bayesiannetwork that can combine observations drawn from sensor data such assensor data that includes HKLD and STOR sensor data, as well as, forexample, data as to block movements, estimated distance to bottom ofhole and state (e.g., at a previous timestamp or previous timestamps).

As an example, a system can be implemented using one or more processorsand memory accessible thereto where the system includes one or moreinterfaces that are operatively coupled to one or more transmissionmedia (e.g., wire, wireless, etc.) for acquiring sensor data. As anexample, such a system can be operatively coupled to one or more displaysuch that information can be rendered thereto (e.g., one or more GUIs,etc.). As an example, a system can include one or more input devices orinput mechanisms (e.g., touchscreen, stylus, mouse, trackball, voice viamicrophone, etc.). As an example, a user may interact with a GUI or GUIsvia one or more of such input devices and/or input mechanisms. As anexample, a system can include an interface or interfaces that cantransmit signals (e.g., commands, instructions, etc.) to equipment suchas field equipment that can include rigsite equipment that can cause theequipment or a piece thereof to operate in a particular manner (e.g., toperform an action, alter an action, halt an action, etc.). As anexample, a system can be implemented in a drilling operation where thesystem can be a control system that acts to control one or more actionsassociated with drilling. In such an example, the system can include aBayesian network backed by mixture model where the system operates inreal-time, adaptively, to detect one or more system states from drillingtime series, which can include complex temporally correlated patterns.

FIG. 27 an example GUI 2700 that shows various types of data that may beutilized, as acquired during one or more drilling operations. Such datacan include, for example, one or more of HKLD data, STOR data, etc. Asshown, information may be organized with respect to such data for outputto a display, for example, as probabilities that data belong to a HKLDcluster, a STOR cluster, etc., and/or as probabilities for an underlyingmodel on HKLD to be increasing as well as decreasing (e.g., as well asfor STOR) and/or as probabilities of each bit interaction with rockstate after fusion of observables.

By learning from data and, for example, using priors from domainexperts, a system can perform inference in a manner that operatesoptionally without user tuned thresholds or parameters. A probabilisticBayesian approach can provides a framework for dealing with uncertaintyin drilling systems and can optionally be extended with additionalobservations drawn from one or more other measurements (e.g., newmeasurements, existing measurements, periodic measurements, etc.).

FIG. 28 shows an example of a GUI 2800 that is rendered to a display2801, as represented by a dashed line. As shown, the GUI 2800 caninclude various types of information as based on various types of data.For example, weight on bit (WOB) and hookload (HKLD) information can berendered, block position (BPOS), surface torque (STOR), flow rate, rateof penetration (ROP), stick-slip, etc. As shown, a graphic of a portionof a drillstring that includes a bit can be rendered, along with, forexample, information such as bit depth and hole depth. As shown, the GUI2800 includes information as to rig state, which is indicated to be“rotary drilling”, which can be a state for which the bit is engagedwith rock at the bottom of a borehole. As an example, the system 1500 ofFIG. 15 or the system 2600 of FIG. 26 can be utilized to output aninference, which may be an inference as to a relationship between a bitand rock. In such an example, a GUI such as the GUI 2800 may render to adisplay information as to the inference. For example, consider the rigstate field being a state that is an inferred state as output by thesystem 1500 of FIG. 15, the system 2600 of FIG. 26 or a method such asthe method 1000 of FIG. 10.

As an example, modes and/or trends may be rendered. For example,consider a mode rendered proximate to the hookload information, a moderendered next to the surface torque information, and/or a trend renderedproximate to the block position information. As an example, the GUI 2800can include one or more graphical controls that allow for interactiontherewith to, for example, cause the rendering of a GUI such as the GUI1200 of FIG. 12, which shows modes identified via learning for a GMM. Insuch an example, a user may click on the hookload window and cause therendering of one or more of the plots shown in the GUI 1200 of FIG. 12.In such an approach, a user may see what modes are being recognized(e.g., learned) from the data and utilized by a GMM analysis of the datain a process that can infer a relationship between a bit and rock. Suchan approach may provide a user with some degree of confidence that asystem such as the system 1500 of FIG. 15 is operating properly.

As another example, consider a block position graphical control that isproximate to the block position window and selectable by a userinteraction therewith to cause rendering of a detection engine's outputsuch as that of the GUI 1400 of FIG. 14. In such an example, a user canassess how the detection engine is operating with respect toprobabilities of one or more trends. Such an approach may provide a userwith some degree of confidence that a system such as the system 1500 ofFIG. 15 is operating properly.

As an example, a method can include acquiring data during rig operationswhere the rig operations include operations that utilize a bit to drillrock and where the data include different types of data; analyzing thedata utilizing a probabilistic mixture model for modes, a detectionengine for trends and a network model for an inference based at least inpart on at least one of a mode and a trend; and outputting informationas to the inference where the inference characterizes a relationshipbetween the bit and the rock. In such an example, the probabilisticmixture model can be or include a Gaussian mixture model. For example,consider a Gaussian mixture model that includes multiple modes whereeach of the modes represents an operational state of the rig operations.

As an example, outputting information as to an inference can includerendering information to a display, which may be in the form of a GUI.For example, a coded plot may be rendered that indicates a relationshipbetween bit and rock where the relationship is an inferred relationship.In such an example, one or more types of information may be rendered,which may include information as in one or more of the GUIs describedherein. As an example, consider the GUIs 2210, 2230, 2250 and 2270 ofFIG. 22 where the GUI 2270 includes information as to relationshipsbetween a bit and rock (e.g., fully engaged, no interaction, rampingdown and ramping up). In the examples of FIG. 22, coding may be utilizedwith respect to one or more of the inferences, the hookload, the surfacetorque and the block position (e.g., with respect to time). Such anapproach can allow a user (e.g., a driller) to understand operations. Asan example, the GUI 2270 can be associated with one or more controlmechanisms that can issue one or more control signals to one or morepieces of equipment at a rigsite. Such an approach may help to improvedrilling as to, for example, one or more of rate of penetration (ROP),longevity of downhole equipment, and reduction of non-productive time(NPT). Such an approach may help to reduce tripping (e.g., tripping outto replace a piece of equipment on a drillstring such as a bit, etc.).

As an example, a detection engine can output a probability of anoperational trend of rig operations (e.g., operations with respect totime). For example, consider data that include block position data for ablock of a rig performing rig operations and where the operational trendincludes a block position trend of the block of the rig. In such anexample, the block position trend can be a member selected from a groupthat includes decreasing block position and increasing block position.

As an example, data can include hookload data and a probabilisticmixture model can include modes that include an on bottom mode and anoff bottom mode.

As an example, data can include torque data and a probabilistic mixturemodel can include modes that include an off bottom rotating mode and anon bottom rotating mode.

As an example, an inference can be an inference selected from a groupthat includes a bit being engaged with rock at a bottom of a boreholeand the bit having no interaction with the rock at the bottom of theborehole.

As an example, a method can include issuing a control signal based atleast in part on an inference. For example, issuing can issue thecontrol signal to equipment at a rigsite where rig operations are beingperformed. As an example, a method can include controlling at least onepiece of equipment to perform at least one of a plurality of rigoperations based at least in part on an issued control signal that isbased at least in part on an inference.

As an example, a method can include acquiring data in real-time andoutputting information in near real-time (e.g., accounting forcomputational delay and/or other electronic delays, which may be of theorder of ten minutes or less). In such an example, a method can includecontrolling at least one of a plurality of rig operations based at leastin part on the information and acquiring additional data that includesinformation responsive to the controlling. In such an example, acontroller or control system can effectuate a control loop, where thecontrol loop involves use of one or more PMMs and one or more BNNs and,for example, one or more detection engines.

As an example, a system can include a processor; memory accessible tothe processor; processor-executable instructions stored in the memoryand executable by the processor to instruct the system to: acquire dataduring rig operations where the rig operations include operations thatutilize a bit to drill rock and where the data include different typesof data; analyze the data utilizing a probabilistic mixture model formodes, a detection engine for trends and a network model for aninference based at least in part on at least one of a mode and a trend;and output information as to the inference where the inferencecharacterizes a relationship between the bit and the rock. In such anexample, the probabilistic mixture model (PMM) can be or include aGaussian mixture model (GMM). In such a system, as an example, adetection engine can output a probability of an operational trend of therig operations.

As an example, one or more computer-readable storage media can includecomputer-executable instructions, executable to instruct a computer to:acquire data during rig operations where the rig operations includeoperations that utilize a bit to drill rock and where the data includedifferent types of data; analyze the data utilizing a probabilisticmixture model for modes, a detection engine for trends and a networkmodel for an inference based at least in part on at least one of a modeand a trend; and output information as to the inference where theinference characterizes a relationship between the bit and the rock. Insuch an example, the probabilistic mixture model (PMM) can be or includea Gaussian mixture model (GMM). As an example, a detection engine canoutput a probability of an operational trend of rig operations (e.g.,with respect to time).

In some embodiments, a method or methods may be executed by a computingsystem. FIG. 29 shows an example of a system 2900 that can include oneor more computing systems 2901-1, 2901-2, 2901-3 and 2901-4, which maybe operatively coupled via one or more networks 2909, which may includewired and/or wireless networks.

As an example, a system can include an individual computer system or anarrangement of distributed computer systems. In the example of FIG. 29,the computer system 2901-1 can include one or more modules 2902, whichmay be or include processor-executable instructions, for example,executable to perform various tasks (e.g., receiving information,requesting information, processing information, simulation, outputtinginformation, etc.).

As an example, a module may be executed independently, or incoordination with, one or more processors 2904, which is (or are)operatively coupled to one or more storage media 2906 (e.g., via wire,wirelessly, etc.). As an example, one or more of the one or moreprocessors 2904 can be operatively coupled to at least one of one ormore network interface 2907. In such an example, the computer system2901-1 can transmit and/or receive information, for example, via the oneor more networks 2909 (e.g., consider one or more of the Internet, aprivate network, a cellular network, a satellite network, etc.).

As an example, the computer system 2901-1 may receive from and/ortransmit information to one or more other devices, which may be orinclude, for example, one or more of the computer systems 2901-2, etc. Adevice may be located in a physical location that differs from that ofthe computer system 2901-1. As an example, a location may be, forexample, a processing facility location, a data center location (e.g.,server farm, etc.), a rig location, a wellsite location, a downholelocation, etc.

As an example, a processor may be or include a microprocessor,microcontroller, processor module or subsystem, programmable integratedcircuit, programmable gate array, or another control or computingdevice.

As an example, the storage media 2906 may be implemented as one or morecomputer-readable or machine-readable storage media. As an example,storage may be distributed within and/or across multiple internal and/orexternal enclosures of a computing system and/or additional computingsystems.

As an example, a storage medium or storage media may include one or moredifferent forms of memory including semiconductor memory devices such asdynamic or static random access memories (DRAMs or SRAMs), erasable andprogrammable read-only memories (EPROMs), electrically erasable andprogrammable read-only memories (EEPROMs) and flash memories, magneticdisks such as fixed, floppy and removable disks, other magnetic mediaincluding tape, optical media such as compact disks (CDs) or digitalvideo disks (DVDs), BLUERAY® disks, or other types of optical storage,or other types of storage devices.

As an example, a storage medium or media may be located in a machinerunning machine-readable instructions, or located at a remote site fromwhich machine-readable instructions may be downloaded over a network forexecution.

As an example, various components of a system such as, for example, acomputer system, may be implemented in hardware, software, or acombination of both hardware and software (e.g., including firmware),including one or more signal processing and/or application specificintegrated circuits.

As an example, a system may include a processing apparatus that may beor include a general purpose processors or application specific chips(e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriatedevices.

FIG. 30 shows components of a computing system 3000 and a networkedsystem 3010. The system 3000 includes one or more processors 3002,memory and/or storage components 3004, one or more input and/or outputdevices 3006 and a bus 3008. According to an embodiment, instructionsmay be stored in one or more computer-readable storage media (e.g.,memory/storage components 3004). Such instructions may be read by one ormore processors (e.g., the processor(s) 3002) via a communication bus(e.g., the bus 3008), which may be wired or wireless. The one or moreprocessors may execute such instructions to implement (wholly or inpart) one or more attributes (e.g., as part of a method). A user mayview output from (e.g., via a display) and interact with a process viaan I/O device (e.g., the device 3006). According to an embodiment, acomputer-readable storage medium may be a storage component such as aphysical memory storage device, for example, a chip, a chip on apackage, a memory card, etc.

According to an embodiment, components may be distributed, such as inthe network system 3010. The network system 3010 includes a network 3020and components 3022-1, 3022-2, 3022-3, . . . 3022-N. For example, thecomponents 3022-1 may include the processor(s) 3002 while thecomponent(s) 3022-3 may include memory accessible by the processor(s)3002. Further, the component(s) 3022-2 may include an I/O device fordisplay and optionally interaction with a method. The network may be orinclude the Internet, an intranet, a cellular network, a satellitenetwork, etc.

As an example, a device may be a mobile device that includes one or morenetwork interfaces for communication of information. For example, amobile device may include a wireless network interface (e.g., operablevia IEEE 802.11, ETSI GSM, BLUETOOTH®, satellite, etc.). As an example,a mobile device may include components such as a main processor, memory,a display, display graphics circuitry (e.g., optionally including touchand gesture circuitry), a SIM slot, audio/video circuitry, motionprocessing circuitry (e.g., accelerometer, gyroscope), wireless LANcircuitry, smart card circuitry, transmitter circuitry, GPS circuitry,and a battery. As an example, a mobile device may be configured as acell phone, a tablet, etc. As an example, a method may be implemented(e.g., wholly or in part) using a mobile device. As an example, a systemmay include one or more mobile devices.

As an example, a system may be a distributed environment, for example, aso-called “cloud” environment where various devices, components, etc.interact for purposes of data storage, communications, computing, etc.As an example, a device or a system may include one or more componentsfor communication of information via one or more of the Internet (e.g.,where communication occurs via one or more Internet protocols), acellular network, a satellite network, etc. As an example, a method maybe implemented in a distributed environment (e.g., wholly or in part asa cloud-based service).

As an example, information may be input from a display (e.g., consider atouchscreen), output to a display or both. As an example, informationmay be output to a projector, a laser device, a printer, etc. such thatthe information may be viewed. As an example, information may be outputstereographically or holographically. As to a printer, consider a 2D ora 3D printer. As an example, a 3D printer may include one or moresubstances that can be output to construct a 3D object. For example,data may be provided to a 3D printer to construct a 3D representation ofa subterranean formation. As an example, layers may be constructed in 3D(e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example,holes, fractures, etc., may be constructed in 3D (e.g., as positivestructures, as negative structures, etc.).

Although only a few examples have been described in detail above, thoseskilled in the art will readily appreciate that many modifications arepossible in the examples. Accordingly, all such modifications areintended to be included within the scope of this disclosure as definedin the following claims. In the claims, means-plus-function clauses areintended to cover the structures described herein as performing therecited function and not only structural equivalents, but alsoequivalent structures. Thus, although a nail and a screw may not bestructural equivalents in that a nail employs a cylindrical surface tosecure wooden parts together, whereas a screw employs a helical surface,in the environment of fastening wooden parts, a nail and a screw may beequivalent structures. It is the express intention of the applicant notto invoke 35 U.S.C. § 112(f) for any limitations of any of the claimsherein, except for those in which the claim expressly uses the words“means for” together with an associated function.

1. A method comprising: acquiring data during rig operations wherein therig operations comprise operations that utilize a bit to drill rock andwherein the data comprise different types of data; analyzing the datautilizing a probabilistic mixture model for modes, a detection enginefor trends and a network model for an inference based at least in parton at least one of a mode and a trend; and outputting information as tothe inference wherein the inference characterizes a relationship betweenthe bit and the rock.
 2. The method of claim 1 wherein the probabilisticmixture model comprises a Gaussian mixture model.
 3. The method of claim2 wherein the Gaussian mixture model comprises multiple modes whereineach of the modes represents an operational state of the rig operations.4. The method of claim 1 wherein the detection engine outputs aprobability of an operational trend of the rig operations.
 5. The methodof claim 4 wherein the data comprise block position data for a block ofa rig performing the rig operations and wherein the operational trendcomprises a block position trend of the block of the rig.
 6. The methodof claim 5 wherein the block position trend comprises a member selectedfrom a group that comprises decreasing block position and increasingblock position.
 7. The method of claim 1 wherein the data comprisehookload data and wherein the probabilistic mixture model comprisesmodes that comprise an on bottom mode and an off bottom mode.
 8. Themethod of claim 1 wherein the data comprise torque data and wherein theprobabilistic mixture model comprises modes that comprise an off bottomrotating mode and an on bottom rotating mode.
 9. The method of claim 1wherein the inference comprises an inference selected from a group thatcomprises the bit being engaged with the rock at a bottom of a boreholeand the bit having no interaction with the rock at the bottom of theborehole.
 10. The method of claim 1 comprising issuing a control signalbased at least in part on the inference.
 11. The method of claim 10wherein the issuing issues the control signal to equipment at a rigsitewhere the rig operations are being performed.
 12. The method of claim 1comprising acquiring the data in real-time and outputting theinformation in near real-time.
 13. The method of claim 12 comprisingcontrolling at least one of the rig operations based at least in part onthe information and acquiring additional data that comprises informationresponsive to the controlling.
 14. A system comprising: a processor;memory accessible to the processor; processor-executable instructionsstored in the memory and executable by the processor to instruct thesystem to: acquire data during rig operations wherein the rig operationscomprise operations that utilize a bit to drill rock and wherein thedata comprise different types of data; analyze the data utilizing aprobabilistic mixture model for modes, a detection engine for trends anda network model for an inference based at least in part on at least oneof a mode and a trend; and output information as to the inferencewherein the inference characterizes a relationship between the bit andthe rock.
 15. (canceled)
 16. A non-transitory computer-readable mediumcomprising a plurality of computer-readable instructions for: acquiringdata during rig operations wherein the rig operations compriseoperations that utilize a bit to drill rock and wherein the datacomprise different types of data; analyzing the data utilizing aprobabilistic mixture model for modes, a detection engine for trends anda network model for an inference based at least in part on at least oneof a mode and a trend; and outputting information as to the inferencewherein the inference characterizes a relationship between the bit andthe rock.
 17. The non-transitory computer-readable medium of claim 16,wherein the inference comprises an inference selected from a group thatcomprises the bit being engaged with the rock at a bottom of a boreholeand the bit having no interaction with the rock at the bottom of theborehole.
 18. The non-transitory computer-readable medium of claim 16,comprising issuing a control signature based at least in part on theinference.
 19. The non-transitory computer-readable medium of claim 18,wherein the issuing issues the control signal to equipment at a rigsitewhere the rig operations are being performed.
 20. The non-transitorycomputer-readable medium of claim 16, comprising acquiring the data inreal-time and outputting the information in near real-time.